Methods and systems for generating a vibrant compatibility plan using artificial intelligence

ABSTRACT

A system for generating a vibrant compatibility plan using artificial intelligence. The system includes at least a server wherein the at least a server is designed and configured to receive at least a composition datum from a user client device wherein the at least a composition datum includes at least an element of user body data and at least an element of desired dietary state data. At least a server is configured to select at least a correlated dataset. At least a server is configured to create at least an unsupervised machine-learning model including at least a hierarchical clustering model to output at least a compatible food element. At least a server is configured to generate at least a vibrant compatibility plan wherein the at least a vibrant compatibility plan further comprises a plurality of compatible food elements each containing at least a food element compatibility index value score as a function of the at least a hierarchical clustering model.

FIELD OF THE INVENTION

The present invention generally relates to the field of artificialintelligence. In particular, the present invention is directed tomethods and systems for generating a vibrant compatibility plan usingartificial intelligence.

BACKGROUND

Accurate analysis of datasets can be challenging due to the vast amountof data to be analyzed. Incorrect analysis can lead to inaccuracies andfrustrate users. Ensuring accurate selection and implementation isimportant.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for generating a vibrant compatibility plan usingartificial intelligence. The system includes at least a server. The atleast a server is designed and configured to receive at least acomposition datum from a user client device, generated as a function ofat least a user conclusive label and at least a user dietary response,wherein the at least a composition datum further comprises at least anelement of user body data and at least an element of desired dietarystate data and the at least a user conclusive label contains at least anincompatible food element generated as a function of at least aconclusive label neutralizer. The at least a server is designed andconfigured to select at least a correlated dataset containing aplurality of data entries wherein each dataset contains at least a datumof body data and at least a correlated compatible food element as afunction of the at least a composition datum. The at least a server isdesigned and configured to create at least an unsupervisedmachine-learning model wherein the at least an unsupervisedmachine-learning model further comprises generating a hierarchicalclustering model to output at least a compatible food element as afunction of the at least a composition datum and the at least acorrelated dataset. The at least a server is designed and configured togenerate at least a vibrant compatibility plan wherein the at least avibrant compatibility plan further comprises a plurality of compatiblefood elements each containing at least a food element compatibilityindex value score as a function of the at least a hierarchicalclustering model.

In an aspect, a method of generating a vibrant compatibility plan usingartificial intelligence. The method includes receiving by at least aserver at least a composition datum from a user client device, generatedas a function of at least a user conclusive label and at least a userdietary response, wherein the at least a composition datum furthercomprises at least an element of user body data and at least an elementof desired dietary state data and the at least a user conclusive labelcontains at least an incompatible food element generated as a functionof at least a conclusive label neutralizer. The method includesselecting by the at least a server at least a correlated datasetcontaining a plurality of data entries wherein each dataset contains atleast datum of body data and at least a correlated compatible foodelement as a function of the at least a composition datum. The methodincludes creating by the at least a server at least an unsupervisedmachine-learning model wherein the at least an unsupervisedmachine-learning algorithm further comprises generating a hierarchicalclustering model to output at least a compatible food element as afunction of the at least a composition datum and the at least acorrelated dataset. The method includes generating by the at least aserver at least a vibrant compatibility plan wherein the at least avibrant compatibility plan further comprises a plurality of compatiblefood elements each containing a least a food element compatibility indexvalue score as a function of the at least a hierarchical clusteringmodel.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of asystem for generating a vibrant compatibility plan using artificialintelligence;

FIG. 2 is a block diagram illustrating an exemplary embodiment of a bodydatabase;

FIG. 3 is a block diagram illustrating an exemplary embodiment of anexpert knowledge database;

FIG. 4 is a block diagram illustrating an exemplary embodiment of anunsupervised learning module;

FIG. 5 is a block diagram illustrating an exemplary embodiment of ahierarchical clustering model;

FIG. 6 is a block diagram illustrating an exemplary embodiment of ahealth record database;

FIG. 7 is a block diagram illustrating an exemplary embodiment of a foodelement compatibility index value database;

FIG. 8 is a block diagram illustrating an exemplary embodiment of atable contained within food element compatibility index value database;

FIG. 9 is a block diagram illustrating an exemplary embodiment of a foodelement profile database;

FIG. 10 is a block diagram illustrating an exemplary embodiment of asupervised learning module;

FIG. 11 is a block diagram illustrating an exemplary embodiment of atraining set database;

FIG. 12 is a block diagram illustrating an exemplary embodiment of afilter database;

FIG. 13 is a process flow diagram illustrating an exemplary embodimentof a method of generating a vibrant compatibility plan using artificialintelligence; and

FIG. 14 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for generating a vibrant compatibility plan usingartificial intelligence. In an embodiment, at least a server receives atleast a composition datum from a user client device wherein the at leasta composition datum includes at least an element of user body data andat least an element of desired dietary state data. In an embodiment, atleast an element of user body data may include a nutritional biomarkersuch as a blood testing indicating the absence or presence of aparticular gene. At least a server selects at least a correlated datasetcontaining a plurality of data entries wherein each dataset contains atleast a datum of body data and at least a correlated compatible foodelement as a function of the at least a composition datum. For example,at least a server may select at least a dataset that contains body datathat may match body data contained within at least a composition datumsuch as the same nutritional biomarker. At least a server creates atleast an unsupervised machine-learning model wherein the at least anunsupervised machine-learning model further comprises generating ahierarchical clustering model to output at least a compatible foodelement as a function of the at least a composition datum and the atleast a correlated dataset. At least a server generates at least avibrant compatibility plan wherein the at least a vibrant compatibilityplan further comprises a plurality of compatible food elements eachcontaining at least a food element compatibility index value score as afunction of the at least a hierarchical clustering.

Turning now to FIG. 1, a system 100 for generating a vibrantcompatibility plan using artificial intelligence is illustrated. System100 includes at least a server 104. At least a server 104 may includeany computing device as described herein, including without limitation amicrocontroller, microprocessor, digital signal processor (DSP) and/orsystem on a chip (SoC) as described herein. At least a server 104 may behoused with, may be incorporated in, or may incorporate one or moresensors of at least a sensor. Computing device may include, be includedin, and/or communicate with a mobile device such as a mobile telephoneor smartphone. At least a server 104 may include a single computingdevice operating independently, or may include two or more computingdevice operating in concert, in parallel, sequentially or the like; twoor more computing devices may be included together in a single computingdevice or in two or more computing devices. At least a server 104 withone or more additional devices as described below in further detail viaa network interface device. Network interface device may be utilized forconnecting a at least a server 104 to one or more of a variety ofnetworks, and one or more devices. Examples of a network interfacedevice include, but are not limited to, a network interface card (e.g.,a mobile network interface card, a LAN card), a modem, and anycombination thereof. Examples of a network include, but are not limitedto, a wide area network (e.g., the Internet, an enterprise network), alocal area network (e.g., a network associated with an office, abuilding, a campus or other relatively small geographic space), atelephone network, a data network associated with a telephone/voiceprovider (e.g., a mobile communications provider data and/or voicenetwork), a direct connection between two computing devices, and anycombinations thereof. A network may employ a wired and/or a wirelessmode of communication. In general, any network topology may be used.Information (e.g., data, software etc.) may be communicated to and/orfrom a computer and/or a computing device. At least a server 104 mayinclude but is not limited to, for example, a at least a server 104 orcluster of computing devices in a first location and a second computingdevice or cluster of computing devices in a second location. At least aserver 104 may include one or more computing devices dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. At least a server 104 may distribute one or more computing tasksas described below across a plurality of computing devices of computingdevice, which may operate in parallel, in series, redundantly, or in anyother manner used for distribution of tasks or memory between computingdevices. At least a server 104 may be implemented using a “sharednothing” architecture in which data is cached at the worker, in anembodiment, this may enable scalability of system 100 and/or computingdevice.

With continued reference to FIG. 1, at least a server 104 is configuredto receive at least a composition datum 108 from a user client device112 generated as a function of at least a user conclusive label and atleast a user dietary response, wherein the at least a composition datumfurther comprises at least an element of user body data and at least anelement of desired dietary state data. Composition datum 108, as usedherein, includes any data describing and/or relating to a nutritionalstate of a user. Nutritional state, as used herein includes the dietaryrequirements of a user and any associated nutrient levels, nutrientbiomarkers, and/or biological samples obtained from a physicallyextracted sample of a user. Physically extracted sample, as used herein,includes a sample obtained by removing and analyzing tissue and/orfluid. Physically extracted sample may include without limitation ablood sample, a tissue sample, a buccal swab, a mucous sample, a stoolsample, a hair sample, a fingernail sample, or the like. Physicallyextracted sample may include, as a non-limiting example, at least ablood sample. As a further non-limiting example, at least a biologicalextraction may include at least a genetic sample. At least a geneticsample may include a complete genome of a person or any portion thereof.At least a genetic sample may include a DNA sample and/or an RNA sample.At least a physically extracted sample may include an epigenetic sample,a proteomic sample, a tissue sample, a biopsy, and/or any otherphysically extracted sample. At least a biological extraction mayinclude an endocrinal sample. As a further non-limiting example, the atleast a biological extraction may include a signal from at least asensor configured to detect physiological data of a user and recordingthe at least a biological extraction as a function of the signal. Atleast a sensor may include any sensor and/or device configured tocapture sensor data concerning a patient, including any scanning,radiological and/or imaging device such as without limitation x-rayequipment, computer assisted tomography (CAT) scan equipment, positronemission tomography (PET) scan equipment, any form of magnetic resonanceimagery (MRI) equipment, ultrasound equipment, optical scanningequipment such as photo-plethysmographic equipment, or the like. Atleast a sensor may include any electromagnetic sensor, including withoutlimitation electroencephalographic sensors, magnetoencephalographicsensors, electrocardiographic sensors, electromyographic sensors, or thelike. At least a sensor may include a temperature sensor. At least asensor may include any sensor that may be included in a mobile deviceand/or wearable device, including without limitation a motion sensorsuch as an inertial measurement unit (IMU), one or more accelerometers,one or more gyroscopes, one or more magnetometers, or the like. At leasta wearable and/or mobile device sensor may capture step, gait, and/orother mobility data, as well as data describing activity levels and/orphysical fitness. At least a wearable and/or mobile device sensor maydetect heart rate or the like. At least a sensor may detect anyhematological parameter including blood oxygen level, pulse rate, heartrate, pulse rhythm, and/or blood pressure. At least a sensor may be apart of at least a server 104 or may be a part of a separate device incommunication with at least a server 104.

With continued reference to FIG. 1, nutritional state may include anydata indicative of a person's nutritional biomarkers; nutritional statemay be evaluated with regard to one or more measures of health of aperson's body, one or more systems within a person's body such as adigestive system, a circulatory system, or the like, one or more organswithin a person's body, one or more body dimensions of a person's body;and/or any other subdivision of a person's body useful for nutritionalevaluation. Nutritional biomarkers may include, without limitation, oneor more intracellular or extracellular measurements of vitamin levelssuch as Vitamin A, Vitamin B2, Vitamin D and the like. Nutritionalbiomarkers may include, without limitation, one or more intracellular orextracellular measurements of mineral levels such as calcium, magnesium,and iron. Nutritional biomarkers may include, without limitation, one ormore intracellular or extracellular measurements of metabolites such ascholine, inositol, and carnitine. Nutritional biomarkers may include;without limitation, one or more intracellular or extracellularmeasurements of electrolytes such as sodium and potassium. Nutritionalbiomarkers may include, without limitation, one or more intracellular orextracellular measurements of amino acids such as asparagine, glutamine,and serine. Nutritional biomarkers may include, without limitation, oneor more intracellular or extracellular measurements of antioxidants suchas Coenzyme Q10, cysteine, and glutathione. Nutritional biomarkers mayinclude, without limitation, one or more intracellular or extracellularmeasurements of fatty acids and omega acids such as eicosatetraenoicacid (EPA), docosahexaenoic acid (DHA), and total omega 3 levels.Nutritional biomarkers may include, without limitation, one or moremicrobiome measurements such as stool tests that identify microorganismsliving within a gut such as strains and/or quantities of bacteria,archaea, fungi, protozoa, algae, viruses; parasites, words, and thelike. Nutritional biomarker may include, without limitation, one or moregenetic measurements such as APOE gene that is involved intransportation of blood lipids such as cholesterol or MTHFR gene that isinvolved in making enzymes involved in metabolism and utilization ofVitamin B9 or FTO gene that is involved in a user's ability to feel fullor satiated. Nutritional biomarker may include, without limitation, oneor more gut wall measurements such as data describing on gut wallfunction, gut wall integrity, gut wall strength, gut wall absorption,gut wall permeability, intestinal absorption, gut wall absorption ofbacteria. Gut wall measurements may include for example blood levels ofcreatinine levels or breath levels of lactulose, hydrogen, methane,lactose and the like. Nutritional biomarker may include one or moremeasurement of cognitive function, including any data generated usingpsychological, neuro-psychological and/or cognitive evaluations as wellas diagnostic screening tests; personality tests, personal compatiblytests or the like.

With continued reference to FIG. 1, composition datum 108 includes atleast an element of user body data 116 and at least an element ofdesired dietary state data. User body data 116, as used herein, includesany nutritional biomarker, including any of the nutritional biomarkersas described above. For instance and without limitation, nutritionalbiomarker may include a salivary hormone panel that contains levels ofestradiol, estrone, estriol, progesterone, testosterone, cortisol, andmelatonin. In yet another non-limiting example, nutritional biomarkermay include a blood sample result showing plasma levels of amino acidsand metabolites utilized in folate metabolism and synthesis in the body.In yet another non-limiting example, nutritional biomarker may include auser self-reported previous diagnosis, medical condition, dietaryelimination, self-diagnosis, dietary lifestyle, nutritional belief,nutritional habit and the like. For instance and without limitation,nutritional biomarker may include a user's self-reported glutenintolerance or a user's aversion to consume animal products. Nutritionalbiomarker may include a user's inability to consume a particular food orfood group for religious reasons or an inability to consume a particularfood or food group due to an immunoglobulin E (IGE) mediated response oran immunoglobulin G (IGG) mediated response. In yet another non-limitingexample, nutritional biomarker may include a user's preference toconsume certain foods or a user's self-described dietary eliminationdiet.

With continued reference to FIG. 1, composition datum 108 includes atleast an element of desired dietary state data. Desired dietary statedata, as used herein, includes a user's goal eating habits and/orcurrent eating habits. Eating habits as used herein, includes any datadescribing a user's food preferences, food allergies, food intolerances,style of eating, diet that a user may be following; meal types, and thelike. Desired dietary state data 120 may include for example a user'sgoal to consume a gluten free diet or a diet free of artificial colors.Desired dietary state may be received as a function of a user conclusivelabel.

With continued reference to FIG. 1, at least a composition datum isgenerated as a function of at least a user conclusive label and at leasta user dietary response. User conclusive label as used herein, includesan element of data identifying and/or describing a current, incipient,or probable future medical condition affecting a person; medicalcondition may include a particular disease, one or more symptomsassociated with a syndrome, a syndrome, and/or any other measure ofcurrent or future health and/or healthy aging. For example, userconclusive label may include an element of data indicating a diagnosisof a user from a medical professional such as for example, a diagnosisof celiac disease by a medical professional or a diagnosis of Vitamin Cdeficiency from a nutritionist. User conclusive label may be associatedwith a physical and/or somatic condition, a mental condition, a chronicinfection, an immune disorder, a metabolic disorder, a connective tissuedisorder, an excretory system disorder, a liver disorder, a jointdisorder, a cancer, and the like. User conclusive label may beassociated with a descriptor of latent, dormant, and/or apparentdisorders, diseases, and/or conditions. User conclusive labels mayinclude descriptors of conditions for which a person may have a higherthan average probability of development such as a condition for which aperson may have a “risk factor” such as for example a person sufferingfrom abdominal obesity may have a higher than average probability ofdeveloping type II diabetes. In an embodiment, desired dietary state maybe received as a function of a user conclusive label, such as forexample when a medical professional may prescribe or desire a particularnutritional state linked to a user conclusive label. For instance andwithout limitation, a medical professional may prescribe a particulardietary state such as a vegan diet for a user with heart disease or alow carbohydrate diet for a user with a systemic Candida albicansinfection. In yet another non-limiting example, a medical professionalmay prescribe a particular dietary state such as a diet rich incarbohydrates that contain B Vitamins for a user with alcoholism or adiet free of added sugars for a user with diabetes mellitus type 2.

With continued reference to FIG. 1, at least an element of desireddietary state data may be received as a function of a user generateddietary response. User generated dietary response may include a user'sown preference for a particular desired dietary state. For instance andwithout limitation, user may experience gas, bloating, diarrhea, andnausea after eating dairy containing foods such as milk, cheese, and icecream. In such an instance, user may choose to eliminate such foods andgenerate a desired dietary state datum that includes a preference toconsume a dairy free diet. In yet another non-limiting example, user mayread in a magazine about a paleo diet being able to help individualslose weight, whereby user may choose to follow a paleo diet. In such aninstance, user may generate a desired dietary state datum that includesa preference to consume a paleo diet. In an embodiment, element of userbody data 116 and element of desired dietary state data 120 may containthe same input. For example, element of user body data 116 may include anutritional biomarker that contains a user self-reported elimination offood products containing nightshades and element of desired dietarystate data 120 may contain an input containing a desire to follow anightshade free diet. In an embodiment, user generated dietary responsemay be generated from a survey or questions that a user may answer. Forexample, user may respond to a series of prompted questions asking userabout user's eating habits, user's eating preferences, foods that userroutinely consumes and the like.

With continued reference to FIG. 1, at least a server 104 receives atleast a composition datum from a user client device generated as afunction of at least a user conclusive label and at least a user dietaryresponse. In an embodiment, user conclusive label such as a previousdiagnosis of Multiple Sclerosis may be used to generate a desireddietary state that includes the Swank diet, and an element of user bodydata that includes a previously recorded magnetic resonance image (MRI).In an embodiment, user conclusive label may not contain a description,such as when a user has no known medical conditions or may not take anymedications or supplements. In yet another non-limiting example, a usermay utilize a previous diagnosis from childhood such as an anaphylacticresponse to tree nuts to generate a composition datum that includes adesired dietary state that does not contain tree nuts and an element ofbody data that contains symptoms of an anaphylactic reaction that a userexperiences upon consumption of tree nuts and tree nut containingproducts such as rash, itchiness, hives, and throat swelling. In anembodiment, dietary response may be utilized to generate at least acomposition datum. For example, a user may prefer to eliminate dairyproducts due to experiencing symptoms such as gas, bloating, anddiarrhea after consuming dairy products. In such an instance, user maygenerate a composition datum that includes a body datum such as symptomsuser experiences upon consuming dairy products and dietary state datathat includes a diet free of dairy products. In yet another non-limitingexample, a user may for example, perform a direct to consumer healthtest at home without the supervision of a medical professional thatprovides a user with results of a stool sample analysis containing ananalysis of microbial species that may be present and/or absent withinuser's gastrointestinal tract. In such an instance, user may generate atleast a composition datum containing body datum which includes stoolsample analysis and at least a dietary response that may not select anyone particular dietary state.

With continued reference to FIG. 1, at least a server 104 is configuredto receive at least a user conclusive label containing at least anincompatible food element as a function of at least a conclusive labelneutralizer. Incompatible food element, as used herein, includes anyfood element that a user does not seek to consume. User may not consumea particular food element for religious reasons, ethical concerns,dislike of a particular food, an allergy to a particular food whetherdue to anaphylaxis, an intolerance, symptomology, and the like, and orany other reason that a user may not consume a particular food element.Incompatible food element may be generated as a function of at least aconclusive label neutralizer. Conclusive label neutralizer, as usedherein, includes any process that may improve any physical conditionidentifiable in a conclusive label. Conclusive label neutralizer mayinclude medications, supplements, nutrients, herbal remedies, exerciseprograms, medical procedures, physical therapies, psychologicaltherapies, and the like. For example, conclusive label neutralizer mayinclude a specific medication designed to treat a user's nail fungus orconclusive label neutralizer may include a particular supplementutilized to balance out a user's symptoms of estrogen dominance. Userconclusive label may be generated as a function of a conclusive labelneutralizer, such as when a certain medication, supplement, and/ormedical procedure may be associated with incompatible food elements. Forexample, a conclusive label neutralizer such as a statin medication thatis utilized to reduce total cholesterol levels may be utilized togenerate an incompatible food element that includes grapefruit andgrapefruit containing food products. In yet another non-limitingexample, a conclusive label neutralizer such as doxycycline for acne maybe utilized to generate an incompatible food element that includes dairyproducts. In yet another non-limiting example, a conclusive labelneutralizer such as high-intensity training may be utilized to generatean incompatible food element such as grain products as part of apaleo-centered approach often coupled with high-intensity training.

With continued reference to FIG. 1, at least a server 104 is designedand configured to receive at least a user dietary response containing atleast an acute vibrancy input, at least a chronic vibrancy input, and atleast a longevity vibrancy input. Acute vibrancy input, as used herein,includes any short-term dietary response. For example, acute vibrancyinput may include elimination of certain food elements such as cookies,donuts, and cakes because of an upcoming wedding or event such as agraduation. In yet another non-limiting example, acute vibrancy inputmay include a desire to eliminate all carbohydrates from one's diet forthree months before a spring break trip to Medico. Chronic vibrancyinput, as used herein, includes any chronic dietary response. Forexample, chronic vibrancy input may include elimination of certain foodelements as part of an ongoing health plan or health goal. For example,a chronic vibrancy input may include a chronic elimination of foodelements such as sugar due to an ongoing health plan to lose weight. Inyet another non-limiting example, a chronic vibrancy input may include achronic elimination of nightshades food elements such as tomatoes andeggplant as part of an ongoing elimination diet. Longevity vibrancyinput, as used herein, includes any lifelong dietary response. Forexample, longevity vibrancy input may include a permanent elimination ofsimple carbohydrates for a user with a chronic medical condition such asdiabetes. In yet another non-limiting example, longevity vibrancy inputmay include elimination of food elements high in saturated fat such ascoconut and shrimp for a user with heart disease.

With continued reference to FIG. 1, a user client device 112 mayinclude, without limitation, a display in communication with at least aserver 104; display may include any display as described herein. A userclient device 112 may include an additional computing device, such as amobile device, laptop, desktop computer, or the like; as a non-limitingexample, the user client device 112 may be a computer and/or workstationoperated by a medical professional. Output may be displayed on at leasta user client device 112 using an output graphical user interface 140,as described in more detail below. Transmission to a user client device112 may include any of the transmission methodologies as describedherein.

With continued reference to FIG. 1, at least a server 104 is configuredto select at least a correlated dataset containing a plurality of dataentries wherein each dataset contains at least a datum of body data andat least a correlated compatible food element as a function of the atleast a composition datum 108. Body data, as used herein, includes anyof the data suitable for use as user body data 116 as described above.For instance and without limitation, body data may include blood resultsshowing particular extracellular levels of nutrients such as Vitamin D,Vitamin K, and Vitamin 1. In yet another non-limiting example, body datamay include stool results showing particular biomarkers within agastrointestinal tract such as for example, beneficial short-chain fattyacids (SCFA) with n-butyrate, fecal lactoferrin, beneficial bacteria,additional bacteria and the like. In yet another non-limiting example,body data may include a particular diet or way of eating such as forexample a macrobiotic diet or a low FODMAP diet. Datasets may beselected and contained within body database 124 as described below inmore detail in reference to FIG. 2.

With continued reference to FIG. 1, each dataset contains at least adatum of body data 128 and at least a correlated compatible food element132. Compatible food element as used herein, includes any element ofdata identifying and/or describing any food substance that a user mayconsume as a function of a datum of body data. Food substance, as usedherein, includes any substance consumed to provide nutritional supportfor an organism such as a human being. Food substance may include forexample, a particular food such as kale, cabbage, and chicken. Foodsubstance may include a category of food that may be categorized ashaving a shared characteristic or trait. For example, food substance mayinclude categories such as dairy products, vegetables, animal proteins,seafood, fats, carbohydrates, and the like. In an embodiment, at least adatum of body data is correlated with a compatible food element wherethe element of body data is located in the same data element and/orportion of data element as the body data; for example, and withoutlimitation, an element of body data is correlated with a compatible foodelement where both element of body data and compatible food element arecontained within the same first dataset 164 or are both collected fromthe same user. For instance and without limitation, body data showing anovergrowth of yeast in a user's gastrointestinal tract may be correlatedto a compatible food element such as garlic which is shown to beeffective in killing Candida albicans. In yet another non-limitingexample, body data showing low salivary levels of progesterone may becorrelated to a compatible food element such as pumpkin, sweet potato,and broccoli.

With continued reference to FIG. 1, dataset containing plurality of dataentries wherein each dataset contains at least a datum of body data andat least a correlated compatible food element may be stored in a bodydatabase as described in more detail below in reference to FIG. 2.Dataset may be stored in any suitable data and/or data type. Forinstance and without limitation, dataset may include textual data, suchas numerical, character, and/or string data. Textual data may include astandardized name and/or code for a disease, disorder, or the like;codes may include diagnostic codes and/or diagnosis codes, which mayinclude without limitation codes used in diagnosis classificationsystems such as The International Statistical Classification of Diseasesand Related Health Problems (ICD). In general, there is no limitation onforms textual data or non-textual data used as dataset may take; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various forms which may be suitable for use as datasetconsistently with this disclosure.

With continued reference to FIG. 1, dataset may be stored as image data,such as for example an image of a particular food substance such as aphotograph of a pear or an image of a steak. Image data may be stored invarious forms including for example, joint photographic experts group(PEG), exchangeable image file format (Exif), tagged image file format(TIFF), graphics interchange format (GIF), portable network graphics(PNG), netpbm format, portable bitmap (PBM), portable any map (PNM),high efficiency image file format (HEIF), still picture interchange fileformat (SPIFF), better portable graphics (BPG), drawn filed, enhancedcompression wavelet (ECW), flexible image transport system (FITS), freelossless image format (FLIF), graphics environment manage (GEM),portable arbitrary map (PAM), personal computer exchange (PCX),progressive graphics file (PGF), gerber formats, 2 dimensional vectorformats, 3 dimensional vector formats, compound formats including bothpixel and vector data such as encapsulated postscript (EPS), portabledocument format (PDF), and stereo formats.

With continued reference to FIG. 1, datasets contained within bodydatabase may be obtained from a plurality of sources. Datasets containedwithin body database may contain a plurality of data entries, obtainedfor example, from patient medical records that have been stripped ofidentifying information. Datasets contained within body database may beobtained from patient surveys who may be sampled in a variety of methodssuch as by phone, mail, internet and the like. Patient surveys may bedistributed to patients across a breadth of geographical locations andmay also be stripped of identifying information. Datasets containedwithin body database may be obtained from clinical data such as fromfacilities including nursing homes, hospitals, home health agencies, andthe like as described below in more detail in reference to FIG. 6.

With continued reference to FIG. 1, datasets contained within bodydatabase may be obtained from an expert knowledge database. Expertknowledge database 136 may include data entries reflecting one or moreexpert submissions of data such as may have been submitted according toany process, including without limitation by using graphical userinterface 140. Information contained within expert knowledge database136 may be received from input from expert client device. Expert clientdevice may include any device suitable for use as user client device 112as described above. Expert knowledge database 136 may include one ormore fields generated by a language processing module, such as withoutlimitation fields extracted from one or more documents such as forexample medical journals, scientific journals, medical articles,scientific articles, medical reviews, scientific reviews, medicaltrials, scientific trials and the like. Documents may be stored and/orretrieved by at least a server 104 and/or language processing module 144in and/or from a document database. Documents in document database maybe linked to and/or retrieved using document identifiers such as URIand/or URL data, citation data, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousways in which documents may be indexed and retrieved according tocitation, subject matter, author, data, or the like as consistent withthis disclosure.

With continued reference to FIG. 1, at least a server 104 may receive alist of significant categories of body data and/or compatible foodelements from at least an expert. In an embodiment, at least a server104 may provide a graphical user interface 140, which may includewithout limitation a form or other graphical element having data entryfields, wherein one or more experts, including without limitationclinical and/or scientific experts, may enter information describing oneor more categories of body data that the experts consider to besignificant or useful for detection of conditions; fields in graphicaluser interface 140 may provide options describing previously identifiedcategories, which may include a comprehensive or near-comprehensive listof types of user input datums detectable using known or recorded testingmethods, for instance in “drop-down” lists, where experts may be able toselect one or more entries to indicate their usefulness and/orsignificance in the opinion of the experts. Fields may include free-formentry fields such as text-entry fields where an expert may be able totype or otherwise enter text, enabling expert to propose or suggestcategories not currently recorded. Graphical user interface 140 or thelike may include fields corresponding to correlated compatible foodelements, where experts may enter data describing compatible foods theexperts consider related to entered categories of body data; forinstance, such fields may include drop-down lists or other pre-populateddata entry fields listing currently recorded body data, and which may becomprehensive, permitting each expert to select a compatible foodelement the expert believes to be predicted and/or associated with eachcategory of body data selected by the expert. Fields for entry of bodydata and/or compatible food elements may include free-form data entryfields such as text entry fields; as described above, examiners mayenter data not presented in pre-populated data fields in the free-formdata entry fields. Alternatively or additionally, fields for entry ofbody data and/or compatible food elements may enable an expert to selectand/or enter information describing or linked to a category of body datathat the expert considers significant, where significance may indicatelikely impact on longevity, mortality, quality of life, or the like asdescribed in further detail below. Graphical user interface 140 mayprovide an expert with a field in which to indicate a reference to adocument describing significant categories of body data, relationshipsof such categories to compatible food elements, and/or significantcategories of compatible food elements. Any data described above mayalternatively or additionally be received from experts similarlyorganized in paper form, which may be captured and entered into data ina similar way, or in a textual form such as a portable document file(PDF) with examiner entries, or the like.

With continued reference to FIG. 1, data information describingsignificant categories of body data, relationships of such categories tocompatible food element may be extracted from one or more documentsusing a language processing module 144. Language processing module 144may include any hardware and/or software module. Language processingmodule 144 may be configured to extract, from the one or more documents,one or more words. One or more words may include, without limitation,strings of one or characters, including without limitation any sequenceor sequences of letters, numbers, punctuation, diacritic marks,engineering symbols, geometric dimensioning and tolerancing (GD&T)symbols, chemical symbols and formulas, spaces, whitespace, and othersymbols, including any symbols usable as textual data as describedabove. Textual data may be parsed into tokens, which may include asimple word (sequence of letters separated by whitespace) or moregenerally a sequence of characters as described previously. The term“token,” as used herein, refers to any smaller, individual groupings oftext from a larger source of text; tokens may be broken up by word, pairof words, sentence, or other delimitation. These tokens may in turn beparsed in various ways. Textual data may be parsed into words orsequences of words, which may be considered words as well. Textual datamay be parsed into “n-grams”, where all sequences of n consecutivecharacters are considered. Any or all possible sequences of tokens orwords may be stored as “chains”, for example for use as a Markov chainor Hidden Markov Model.

Still referring to FIG. 1, language processing module 144 may compareextracted words to categories of body data recorded by at least a server104, and/or one or more categories of compatible food substancesrecorded by at least a server 104; such data for comparison may beentered on at least a server 104 as using expert data inputs or thelike. In an embodiment; one or more categories may be enumerated, tofind total count of mentions in such documents. Alternatively oradditionally, language processing module 144 may operate to produce alanguage processing model. Language processing model may include aprogram automatically generated by at least a server 104 and/or languageprocessing module 144 to produce associations between one or more wordsextracted from at least a document and detect associations, includingwithout limitation mathematical associations, between such words, and/orassociations of extracted words with categories of user input datums,relationships of such categories to first probing elements, and/orcategories of first probing elements. Associations between languageelements; where language elements include for purposes herein extractedwords, categories of user input datums, relationships of such categoriesto first probing elements, and/or categories of first probing elementsmay include, without limitation, mathematical associations, includingwithout limitation statistical correlations between any language elementand any other language element and/or language elements. Statisticalcorrelations and/or mathematical associations may include probabilisticformulas or relationships indicating, for instance, a likelihood that agiven extracted word indicates a given category of user input datum, agiven relationship of such categories to a first probing element, and/ora given category of a first probing element. As a further example,statistical correlations and/or mathematical associations may includeprobabilistic formulas or relationships indicating a positive and/ornegative association between at least an extracted word and/or a givencategory of body data, a given relationship of such categories tocompatible food element, and/or a given category of compatible foodelement; positive or negative indication may include an indication thata given document is or is not indicating a category of body data,relationship of such category to a first compatible food element, and/orcategory of compatible food elements is or is not significant. Forinstance, and without limitation, a negative indication may bedetermined from a phrase such as “High stool fungal overgrowth was notfound to be compatible with high fructose corn syrup” whereas a positiveindication may be determined from a phrase such as “Low serum sodiumlevels was found to be compatible with sea salt” as an illustrativeexample; whether a phrase, sentence, word, or other textual element in adocument or corpus of documents constitutes a positive or negativeindicator may be determined, in an embodiment, by mathematicalassociations between detected words, comparisons to phrases and/or wordsindicating positive and/or negative indicators that are stored in memoryby at least a server 104, or the like.

Still referring to FIG. 1, language processing module 144 and/or atleast a server 104 may generate the language processing model by anysuitable method, including without limitation a natural languageprocessing classification algorithm; language processing model mayinclude a natural language process classification model that enumeratesand/or derives statistical relationships between input term and outputterms. Algorithm to generate language processing model may include astochastic gradient descent algorithm, which may include a method thatiteratively optimizes an objective function, such as an objectivefunction representing a statistical estimation of relationships betweenterms, including relationships between input terms and output terms, inthe form of a sum of relationships to be estimated. In an alternative oradditional approach, sequential tokens may be modeled as chains, servingas the observations in a Hidden Markov Model (HMM). HMMs as used herein,are statistical models with inference algorithms that that may beapplied to the models. In such models, a hidden state to be estimatedmay include an association between an extracted word category of bodydata, a given relationship of such categories to compatible foodelement, and/or a given category of compatible food elements. There maybe a finite number of category of body data a given relationship of suchcategories to a compatible food element, and/or a given category of foodelements to which an extracted word may pertain; an HMM inferencealgorithm, such as the forward-backward algorithm or the Viterbialgorithm, may be used to estimate the most likely discrete state givena word or sequence of words. Language processing module 144 may combinetwo or more approaches. For instance, and without limitation,machine-learning program may use a combination of Naive-Bayes (NB),Stochastic Gradient Descent (SGD), and parameter grid-searchingclassification techniques; the result may include a classificationalgorithm that returns ranked associations.

Continuing to refer to FIG. 1, generating language processing model mayinclude generating a vector space, which may be a collection of vectors,defined as a set of mathematical objects that can be added togetherunder an operation of addition following properties of associativity,commutativity, existence of an identity element, and existence of aninverse element for each vector, and can be multiplied by scalar valuesunder an operation of scalar multiplication compatible with fieldmultiplication, and that has an identity element is distributive withrespect to vector addition, and is distributive with respect to fieldaddition. Each vector in an n-dimensional vector space may berepresented by an n-tuple of numerical values. Each unique extractedword and/or language element as described above may be represented by avector of the vector space. In an embodiment, each unique extractedand/or other language element may be represented by a dimension ofvector space; as a non-limiting example, each element of a vector mayinclude a number representing an enumeration of co-occurrences of theword and/or language element represented by the vector with another wordand/or language element. Vectors may be normalized, scaled according torelative frequencies of appearance and/or file sizes. In an embodimentassociating language elements to one another as described above mayinclude computing a degree of vector similarity between a vectorrepresenting each language element and a vector representing anotherlanguage element; vector similarity may be measured according to anynorm for proximity and/or similarity of two vectors, including withoutlimitation cosine similarity, which measures the similarity of twovectors by evaluating the cosine of the angle between the vectors, whichcan be computed using a dot product of the two vectors divided by thelengths of the two vectors. Degree of similarity may include any othergeometric measure of distance between vectors.

Still referring to FIG. 1, language processing module 144 may use acorpus of documents to generate associations between language elementsin a language processing module 144 and at least a server 104 may thenuse such associations to analyze words extracted from one or moredocuments and determine that the one or more documents indicatesignificance of a category of body data, a given relationship of suchcategories to compatible food elements, and/or a given category of foodelements. In an embodiment, at least a server 104 may perform thisanalysis using a selected set of significant documents, such asdocuments identified by one or more experts as representing goodscience, good clinical analysis, or the like; experts may identify orenter such documents via graphical user interface 140, or maycommunicate identities of significant documents according to any othersuitable method of electronic communication, or by providing suchidentity to other persons who may enter such identifications into atleast a server 104. Documents may be entered into at least a server 104by being uploaded by an expert or other persons using, withoutlimitation, file transfer protocol (FTP) or other suitable methods fortransmission and/or upload of documents; alternatively or additionally,where a document is identified by a citation, a uniform resourceidentifier (URI), uniform resource locator (URI) or other datumpermitting unambiguous identification of the document, at least a server104 may automatically obtain the document using such an identifier, forinstance by submitting a request to a database or compendium ofdocuments such as JSTOR as provided by Ithaka Harbors; Inc. of New York.

With continued reference to FIG. 1, at least a server may be configuredto extract at least a physiological trait from at least a compositiondatum 108 and match the at least a physiological treat to at least acorrelated dataset containing at least an element of the at least aphysiological trait. Physiological trait, as used herein, includesinformation, data, and/or description relating a functioning of a user'sbody. Physiological trait may include any data suitable for use as bodydata as described above, including for example any nutritionalbiomarker. For example, physiological data may include a measured bloodvalue of a nutrient such as zinc, a stool test reflecting a bacterialcount, bacterial species, and the like. Physiological trait may includea nutritional biomarker such as a mutation of the congenital lactasedeficiency gene or a mutation of TCF7L2 gene that regulates insulinsecretion.

With continued reference to FIG. 1 physiological trait may be extractedfrom a composition datum 108 by a parsing module 148 operating on atleast a server 104. Parsing module 148 may parse at least a compositiondatum 108 for at least a physiological trait and match the at least aphysiological trait to at least a correlated dataset containing at leastan element of the at least a physiological trait. In an embodiment,datasets contained within body database may be categorized byphysiological traits, as described in more detail below in reference toFIG. 2. Parsing module 148 may match at least a dataset by extractingone or more keywords containing words, phrases, test results, numericalscores, and the like from composition datum 108 and analyze the one ormore keywords utilizing for example, language processing module 144 asdescribed in more detail below. Parsing module 148 may be configured tonormalize one or more words or phrases of user input, wherenormalization signifies a process whereby one or more words or phrasesare modified to match corrected or canonical forms; for instance,misspelled words may be modified to correctly spelled versions, wordswith alternative spellings may be converted to spellings adhering to aselected standard, such as American or British spellings,capitalizations and apostrophes may be corrected, and the like; this maybe performed by reference to one or more “dictionary” data structureslisting correct spellings and/or common misspellings and/or alternativespellings, or the like. Parsing module 148 may perform algorithms andcalculations when analyzing tissue sample analysis and numerical testresults. For instance and without limitation, parsing module 148 mayperform algorithms that may compare test results contained withincomposition datum 108, tissue analysis results, and/or biomarker levelsto normal reference ranges or values. For example, parsing module 148may perform calculations that determine how many standard deviationsfrom normal levels a salvia hormone test containing salivary levels ofprogesterone are from normal reference ranges. In yet anothernon-limiting example, parsing module 148 may perform calculationsbetween different values contained within composition datum 108. Forexample, parsing module 148 may calculate a ratio of progesterone toestradiol levels from a blood test containing a hormone panel that mayinclude progesterone, estradiol, estrone, estriol, and testosteroneserum levels.

With continued reference to FIG. 1, parsing module 148 may extractand/or analyze one or more words or phrases by performing dependencyparsing processes; a dependency parsing process may be a process wherebyparsing module 148 recognizes a sentence or clause and assigns asyntactic structure to the sentence or clause. Dependency parsing mayinclude searching for or detecting syntactic elements such as subjects,objects, predicates or other verb-based syntactic structures, commonphrases, nouns, adverbs, adjectives, and the like; such detectedsyntactic structures may be related to each other using a data structureand/or arrangement of data corresponding, as a non-limiting example, toa sentence diagram, parse tree, or similar representation of syntacticstructure. Parsing module 148 may be configured, as part of dependencyparsing, to generate a plurality of representations of syntacticstructure, such as a plurality of parse trees, and select a correctrepresentation from the plurality; this may be performed, withoutlimitation, by use of syntactic disambiguation parsing algorithms suchas, without limitation, Cocke-Kasami-Younger (CKY), Earley algorithm orChart parsing algorithms. Disambiguation may alternatively oradditionally be performed by comparison to representations of syntacticstructures of similar phrases as detected using vector similarity, byreference to machine-learning algorithms and/or modules.

With continued reference to FIG. 1, parsing module 148 may combineseparately analyzed elements from composition datum 108 to extract andcombine at least a keyword. For example, a first test result orbiomarker reading may be combined with a second test result or biomarkerreading that may be generally analyzed and interpreted together. Forinstance and without limitation, a nutritional biomarker of zinc may bereading and analyzed in combination with a nutritional biomarker readingof copper as excess zinc levels can deplete copper levels. In such aninstance, parsing module 148 may combine nutrition biomarker reading offzinc and biomarker reading of copper and combine both levels to createone keyword. In an embodiment, combinations of tissue sample analysis,keywords, or test results that may be interpreted together may bereceived from input received from experts and may be stored in an expertknowledge database. Expert client device 152 may include any devicesuitable for use as user client device 112 as described above.

With continued reference to FIG. 1, at least a server 104 may include anunsupervised machine-learning module 156 operating on at least a serverand/or on another computing device in communication with at least aserver 104, which may include any hardware or software module. At leasta server is configured to create at least an unsupervised machinelearning model wherein the at least an unsupervised machine learningmodel further comprises a hierarchical clustering model 160 to output atleast a compatible food element as a function of the at least acomposition datum 108 and the at least a correlated dataset. Anunsupervised machine-learning process, as used herein, is a process thatderives inferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship; and/or correlation provided in the data. Forinstance, and without limitation, unsupervised machine learning moduleand/or at least a server 104 may perform an unsupervised machinelearning process on a first data set, which may cluster data of firstdata set according to detected relationships between elements of thefirst data set, including without limitation correlations of elements ofbody data to each other and correlations of compatible food element toeach other; such relations may then be combined with supervised machinelearning results to add new criteria for at supervised machine-learningprocesses as described in more detail below. As a non-limiting,illustrative example; an unsupervised process may determine that a firstbody datum correlates closely with a second body datum, where the firstelement has been linked via supervised learning processes to a givencompatible food element, but the second has not; for instance, thesecond body datum may not have been defined as an input for thesupervised learning process; or may pertain to a domain outside of adomain limitation for the supervised learning process. Continuing theexample a close correlation between first body datum and second bodydatum may indicate that the second body datum is also a good predictorfor the compatible food element; second body datum may be included in anew supervised process to derive a relationship or may be used as asynonym or proxy for the first body datum.

With continued reference to FIG. 1, hierarchical clustering model 160may include any method of cluster analysis which outputs a hierarchy ofclusters. Cluster analysis, as used herein, includes any grouping ofobjects such as datasets in such a way that datasets in the same groupor cluster are more similar to each other than to those in otherclusters. Cluster analysis may include hard clustering and/or softclustering. Hard clustering may include clustering where each datasetbelongs to any particular cluster or not. Soft clustering may includeclustering where each dataset may belong to a cluster to a certaindegree such as a certain percentage of belonging to any given cluster ora likelihood of belonging to a given cluster. Hierarchical clusteringmay group and/or segment datasets with shared attributes to extrapolatealgorithmic relationships. Hierarchical clustering model 160 may includegenerating various algorithms that may work to find clusters that may begenerated based on parameter settings such as distance functions to use,density threshold, and optimal of clusters to generate. Hierarchicalclustering model 160 may include models such as but not limited toconnectivity models, centroid models, distribution models, densitymodels, subspace models, group models, graph-based models, signed graphmodels, neural models, and the like.

With continued reference to FIG. 1, hierarchical clustering model 160may include agglomerative and/or divisive hierarchical clustering.Agglomerative hierarchical clustering may include a bottom-up approachwhereby each observation may start in its own cluster, and pairs ofclusters may be merged as one moves up the hierarchy. Divisivehierarchical clustering may include a top-down approach whereby allobservations may start in one cluster and splits may be performedrecursively moving down the hierarchy.

With continued reference to FIG. 1, at least a server 104 may beconfigured to retrieve at least a food element compatibility index valuecorrelated to at least a food element from a database and rank the atleast a food element as a function of the food compatibility index. Foodelement compatibility index value, as used herein, is a value assignedto a food element indicating a degree of compatibility between a foodelement any given element of food data. Food compatibility index valuemay be stored in a food compatibility index value database operating onat least a server 104. A given food element may contain a plurality ofindex values each correlated to an element of body data. For example, afood element may contain a first index value correlated to a firstelement of body data and the same food element may contain a secondindex value correlated to a second element of body data. Food elementcompatibility index value may be based on a numerical score whereby ahigh numerical value may indicate a high degree of compatibility wherebya low numerical value may indicate a low degree of compatibility. Foodelement compatibility index values may vary for any single variant ofany particular body data. For instance and without limitation, a foodelement such as brie cheese may be correlated to a low food elementcompatibility index value for a first element of body data that shows aG/G genotype of the MCM6 gene that controls production of lactase enzymeand thereby indicates an inability to produce lactase efficiently withthe G/G genotype, whereby brie cheese may be correlated to a higher foodelement compatibility index value for a second element of body data thatshows an A/A genotype of the MCM6 gene that indicates an ability toproduce lactase efficiently. In an embodiment, food elementcompatibility index value may include a score relating compatibilitybetween a first food element and a second food element for any givenbody datum. For example, food element compatibility index value mayinclude a value that reflects the ability for a user with a body datumsuch as a G/G genotype of the FUT2 gene that controls enzyme productionto absorb Vitamin B12 in the digestive tract, to consume a secondcompatible food element such as lamb based on a high compatibility scorefor a first food element such as pork. In such an instance, food elementcompatibility index value may reflect ability to substitute a firstcompatible food element for a second compatible food element and/orability to consume a second compatible food element as a function ofbeing able to consume a first compatible food element for a user with agiven user body datum. In an embodiment, at least a server 104 may rankcompatibility of at least a food element as a function of the at least afood compatibility index value. For instance, a food element with a highfood compatibility index value may be ranked highly compatibly for auser as compared to a food element with a low food compatibility indexvalue. In an embodiment, ranking at least a food element may include ahierarchical ranking that may rank food elements in decreasing and/orincreasing level of compatibility.

With continued reference to FIG. 1, food element compatibility indexvalue may be calculated as a function of at least a composition datum108 and a first food element profile. First food element profile mayinclude information describing a nutrient density score of a foodelement correlated to a given composition datum 108. Nutrient densitymay include a ranking and/or score reflecting the amount and/or quantityof vitamins, minerals, electrolytes, amino acids, antioxidants,micronutrients, fatty acids, and the like contained within a given foodelement and may be stored in a food element profile database 172 asdescribed below in more detail in reference to FIG. 9. For example, anapple may have a high nutrient density score due to the Vitamin C,fiber, calcium, iron, Vitamin A, polyphenols, antioxidants and potassiumcontained with an apple as compared to a hot dog which may have a lowernutrient density score due to the presence of nitrates, excess sodiumand sugar. In an embodiment, nutrient density scores may containnumerical values that may indicate the nutrient density of any givenfood element. In an embodiment, a high numerical nutrient density scoremay indicate a high amount of nutrients contained within a particularfood element as compared to a low numerical nutrient density score whichmay indicate a low amount of nutrients contained within a particularfood element. In an embodiment, nutrient density scores may be containedwithin a database operating on at least a server. In an embodiment,nutrient density scores may be calculated taking into account forexample, absence of certain nutrients known to be beneficial such asvitamins and antioxidants as well as presence of certain nutrients thatmay not be as beneficial such as nitrates, preservatives and artificialingredients for example.

With continued reference to FIG. 1, system 100 may include a supervisedmachine-learning module 176 operating on at least a server 104.Supervised machine-learning module 176 may select a training set fromtraining set database 180 as described below in more detail in referenceto FIG. 11. Supervised module is configured to select at least a firsttraining set 184, create at least a supervised machine learning model188 using the at least a first training set 184 wherein the at least asupervised machine learning model 188 relates body data to compatiblefood elements and generate at least a compatible food element as afunction of the at least a composition datum 108 and the at least afirst training set 184. With continued reference to FIG. 1, at least aserver 104 may select at least a training set from training set database180. Training set database 180 may contain training sets pertaining todifferent categories and classification of information, includingtraining set components which may contain sub-categories of differenttraining sets. In an embodiment, at least a server may select at least afirst training set 184 by categorizing at least a composition datum 108to contain at least a physiological label and select at least a firsttraining set 184 as a function of the at least a physiological label.Physiological label, as used herein, includes any categorization and/orclassification of a user body datum as belonging to a particularphysiological categorization describing a user body datum as belongingto a particular body system. For example and without limitation, bodysystem may include body dimensions which include classification byparticular root cause pillars of disease. Dimension of the human bodymay include epigenetics, gut wall, microbiome, nutrients, genetics, andmetabolism. In an embodiment, training set database 180 may containtraining sets classified to body dimensions. In such an instance,training sets may be classified to more than one body dimension. Forinstance and without limitation, a training set may be classified to gutwall and microbiome. In yet another non-limiting example, a training setmay be classified to nutrients and metabolism. First training set 184may be selected by matching physiological label to a training setcontaining a matching physiological label. For example, a physiologicallabel that contains microbiome may be matched to a training setcontaining a physiological label that contains microbiome. In anembodiment, first training set 184 may include the at least a correlateddataset.

With continued reference to FIG. 1, supervised machine learning model188 may include without limitation model developed using linearregression models. Linear regression models may include ordinary leastsquares regression; which aims to minimize the square of the differencebetween predicted outcomes and actual outcomes according to anappropriate norm for measuring such a difference (e.g. a vector-spacedistance norm); coefficients of the resulting linear equation may bemodified to improve minimization. Linear regression models may includeridge regression methods, where the function to be minimized includesthe least-squares function plus term multiplying the square of eachcoefficient by a scalar amount to penalize large coefficients. Linearregression models may include least absolute shrinkage and selectionoperator (LASSO) models, in which ridge regression is combined withmultiplying the least-squares term by a factor of 1 divided by doublethe number of samples. Linear regression models may include a multi-tasklasso model wherein the norm applied in the least-squares term of thelasso model is the Frobenius norm amounting to the square root of thesum of squares of all terms. Linear regression models may include theelastic net model, a multi-task elastic net model, a least angleregression model, a LARS lasso model, an orthogonal matching pursuitmodel, a Bayesian regression model, a logistic regression model, astochastic gradient descent model, a perceptron model, a passiveaggressive algorithm, a robustness regression model, a Huber regressionmodel, or any other suitable model that may occur to persons skilled inthe art upon reviewing the entirety of this disclosure. Linearregression models may be generalized in an embodiment to polynomialregression models, whereby a polynomial equation (e.g. a quadratic,cubic or higher-order equation) providing a best predicted output/actualoutput fit is sought; similar methods to those described above may beapplied to minimize error functions, as will be apparent to personsskilled in the art upon reviewing the entirety of this disclosure.

Supervised machine-learning algorithms may include without limitation,linear discriminant analysis. Machine-learning algorithm may includequadratic discriminate analysis. Machine-learning algorithms may includekernel ridge regression. Machine-learning algorithms may include supportvector machines, including without limitation support vectorclassification-based regression processes. Machine-learning algorithmsmay include stochastic gradient descent algorithms, includingclassification and regression algorithms based on stochastic gradientdescent. Machine-learning algorithms may include nearest neighbors'algorithms. Machine-learning algorithms may include Gaussian processessuch as Gaussian Process Regression. Machine-learning algorithms mayinclude cross-decomposition algorithms, including partial least squaresand/or canonical correlation analysis. Machine-learning algorithms mayinclude naïve Bayes methods. Machine-learning algorithms may includealgorithms based on decision trees, such as decision tree classificationor regression algorithms. Machine-learning algorithms may includeensemble methods such as bagging meta-estimator, forest of randomizedtress, AdaBoost, gradient tree boosting, and/or voting classifiermethods. Machine-learning algorithms may include neural net algorithms;including convolutional neural net processes.

With continued reference to FIG. 1, supervised machine-learningalgorithms may include using alternatively or additional artificialintelligence methods, including without limitation by creating anartificial neural network, such as a convolutional neural networkcomprising an input layer of nodes; one or more intermediate layers, andan output layer of nodes. Connections between nodes may be created viathe process of “training” the network, in which elements from a trainingdataset are applied to the input nodes, a suitable training algorithm(such as Levenberg-Marquardt, conjugate gradient, simulated annealing,or other algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning. This network may be trained using any training setas described herein; the trained network may then be used to applydetected relationships between elements of user input datums andantidotes.

Training data, as used herein, is data containing correlation that amachine-learning process may use to model relationships between two ormore categories of data elements. For instance, and without limitation,training data may include a plurality of data entries, each entryrepresenting a set of data elements that were recorded, received, and/orgenerated together; data elements may be correlated by shared existencein a given data entry, by proximity in a given data entry, or the like.Multiple data entries in training data may evince one or more trends incorrelations between categories of data elements; for instance, andwithout limitation, a higher value of a first data element belonging toa first category of data element may tend to correlate to a higher valueof a second data element belonging to a second category of data element,indicating a possible proportional or other mathematical relationshiplinking values belonging to the two categories. Multiple categories ofdata elements may be related in training data according to variouscorrelations; correlations may indicate causative and/or predictivelinks between categories of data elements, which may be modeled asrelationships such as mathematical relationships by machine-learningprocesses as described in further detail below, Training data may beformatted and/or organized by categories of data elements, for instanceby associating data elements with one or more descriptors correspondingto categories of data elements. As a non-limiting example, training datamay include data entered in standardized forms by persons or processes,such that entry of a given data element in a given field in a form maybe mapped to one or more descriptors of categories. Elements in trainingdata may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training datamay be provided in fixed-length formats, formats linking positions ofdata to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),enabling processes or devices to detect categories of data.

Alternatively or additionally, and still referring to FIG. 1, trainingdata may include one or more elements that are not categorized; that is,training data may not be formatted or contain descriptors for someelements of data. Machine-learning algorithms and/or other processes maysort training data according to one or more categorizations using, forinstance; natural language processing algorithms, tokenization,detection of correlated values in raw data and the like; categories maybe generated using correlation and/or other processing algorithms. As anon-limiting example, in a corpus of text, phrases making up a number“n” of compound words, such as nouns modified by other nouns, may beidentified according to a statistically significant prevalence ofn-grains containing such words in a particular order; such an n-gram maybe categorized as an element of language such as a “word” to be trackedsimilarly to single words, generating a new category as a result ofstatistical analysis. Similarly, in a data entry including some textualdata, a person's name and/or a description of a medical condition ortherapy may be identified by reference to a list, dictionary, or othercompendium of terms, permitting ad-hoc categorization bymachine-learning algorithms, and/or automated association of data in thedata entry with descriptors or into a given format. The ability tocategorize data entries automatedly may enable the same training data tobe made applicable for two or more distinct machine-learning algorithmsas described in further detail below.

With continued reference to FIG. 1, at least a server 104 is designedand configured to generate at least a vibrant compatibility plan 192wherein the at least a vibrant compatibility plan 192 further comprisesa plurality of compatible food elements each containing at least a foodelement compatibility index value as a function of the at least ahierarchical clustering model 160. Vibrant compatibility plan 192, asused herein; includes any information and data containing a list of foodelements each containing at least a food element compatibility indexvalue. For example and without limitation, vibrant compatibility plan192 may include a list of two hundred food elements each containing atleast a food element compatibility index value. In an embodiment, twohundred food elements may be arranged in a hierarchical manner andranked according to compatibility. For example, vibrant compatibilityplan 192 may include a ranking in order of decreasing compatibility oftwo hundred food elements.

With continued reference to FIG. 1, at least a server 104 is configuredto generate at least a vibrant compatibility plan 192 containing asequencing instruction set wherein the sequencing instruction setcontains at least an optimal combination of at least a first compatiblefood element and at least a second compatible food element as a functionof the at least a desired dietary state. Sequencing instruction set mayinclude information and/or data describing optimal combinations of foodsand ingredients that a user may combine to create meals or that whencombined together may optimize the nutrition of each other as a functionof a user's desired dietary state. For instance and without limitation,sequencing instruction set may include information describing optimalcombinations of a first compatible food element such as red kidney beansand a second compatible food element such as brown rice for a user witha desired dietary state of vegan diet. In yet another non-limitingexample, sequencing instruction set may include information describingoptimal combinations of a first compatible food element such as wildAlaskan salmon with a second compatible food element such as avocado fora user with a desired dietary state of ketogenic diet. In yet anothernon-limiting example, sequencing instruction set may include informationdescribing optimal combinations of a first compatible food element suchas bananas and a second compatible food element such as blueberries fora user with a desired dietary state of low FODMAP diet.

Referring now to FIG. 2, an exemplary embodiment of body database 124 isillustrated, which may be implemented, without limitation, as a hardwareor software module. Body database 124 may be implemented, withoutlimitation, as a relational database, a key-value retrieval datastoresuch as a NOSQL database, or any other format or structure for use as adatastore that a person skilled in the art would recognize as suitableupon review of the entirety of this disclosure. Body database 124 maycontain datasets that may be utilized by unsupervised learning module156 to find trends, cohorts, and shared datasets between data containedwithin body database 128 and composition datum 108. In an embodiment,datasets contained within body database 124 may be categorized and/ororganized according to shared characteristics. For instance and withoutlimitation, one or more tables contained within body database 124 mayinclude training data link table 200; training data link table 200 maycontain information linking datasets contained within body database 124to datasets contained within training set database 180. For example,dataset contained within body database 124 may also be contained withintraining set database 180 which may be linked through training data linktable 200. In yet another non-limiting example, training data link table200 may contain information linking data sets contained within bodydatabase 124 to datasets contained within training set database 180 suchas when dataset and training set may include data sourced from the sameuser or same cohort of users, or when dataset is utilized as trainingset. One or more tables contained within body database 124 may includedemographic table 204; demographic table 204 may include datasetsclassified to demographic information. Demographic information mayinclude datasets describing age, sex, ethnicity, socioeconomic status,education level, marital status, income level, religion, offspringinformation, and the like. One or more tables contained within bodydatabase 124 may include stool sample table 208; stool sample table 208may include datasets classified to stool samples. Stool samples mayinclude datasets describing stool levels of nutrition biomarkersincluding for example; measured values from a stool sample of anaerobes,parasites, firmicutes to Bacteroidetes ratio, absorption, inflammation,sensitivities, parasitology and the like. One or more tables containedwithin body database 124 may include fluid sample table 212; fluidsample table 212 may include datasets classified to fluid samples. Fluidsamples may include datasets describing fluid samples analyzed fornutritional biomarkers including for example urine sample, semen sample,sweat sample, amniotic fluid sample, cerebrospinal fluid, synovial fluidsample, pleural fluid sample, pericardial fluid sample; blood sample,salivary sample, and the like. One or more tables contained within bodydatabase 124 may include diagnosis table 216; diagnosis table 216 mayinclude datasets containing diagnostic information. Diagnosticinformation may include identification of an illness or health problemby a medical professional including for example, an informed advisorsuch as a functional medicine doctor, a nutritionist, an herbalist, anacupuncturist, a dietician, a nurse, and the like. One or more tablescontained within body database 124 may include self-reported table 220;self-reported table 220 may include datasets containing self-reportednutritional states. Self-reported nutritional states may include forexample, a user self-report to eliminate gluten due to bloating or auser self-report to eliminate meat for ethical reasons. One or moretables contained within body database 124 may include tissue sampletable, nutrient biomarker table; chief complaint table, (not pictured).Persons skilled in the art will be aware of the various database tablesthat may be contained within body database 124 consistently within thepurview of this disclosure.

Referring now to FIG. 3, an exemplary embodiment of expert knowledgedatabase 136 is illustrated. Expert knowledge database 136 may includeany data structure for ordered storage and retrieval of data, which maybe implemented as a hardware or software module, and which may beimplemented as any database structure suitable for use as body database124. One or more database tables in expert knowledge database 136 mayinclude, as a non-limiting example, an expert food element compatibilitytable 300. Expert food element compatibility table 300 may be a tablerelating user body data 116 to compatible food elements; for instance,where an expert has entered data relating a user body datum such as hightriglycerides to a compatible food element such as oat bran, one or morerows recording such an entry may be inserted in expert food elementcompatibility table. In an embodiment, a forms processing module 304 maysort data entered in a submission via graphical user interface 140 by,for instance, sorting data from entries in the graphical user interface140 to related categories of data; for instance, data entered in anentry relating in the graphical user interface 140 to a body datum maybe sorted into variables and/or data structures for storage of bodydatums, while data entered in an entry relating to compatible foodelements and/or an element thereof may be sorted into variables and/ordata structures for the storage of, respectively, categories ofcompatible food elements. Where data is chosen by an expert frompre-selected entries such as drop-down lists, data may be storeddirectly; where data is entered in textual form, language processingmodule 144 may be used to map data to an appropriate existing label, forinstance using a vector similarity test or other synonym-sensitivelanguage processing test to map classified biomarker data to an existinglabel. Alternatively or additionally, when a language processingalgorithm, such as vector similarity comparison, indicates that an entryis not a synonym of an existing label, language processing module 144may indicate that entry should be treated as relating to a new label;this may be determined by, e.g., comparison to a threshold number ofcosine similarity and/or other geometric measures of vector similarityof the entered text to a nearest existent label, and determination thata degree of similarity falls below the threshold number and/or a degreeof dissimilarity falls above the threshold number. Data from experttextual submissions 308, such as accomplished by filling out a paper orPDF form and/or submitting narrative information, may likewise beprocessed using language processing module 144. Data may be extractedfrom expert papers 312, which may include without limitationpublications in medical and/or scientific journals, by languageprocessing module 144 via any suitable process as described herein.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various additional methods whereby novelterms may be separated from already-classified terms and/or synonymstherefore, as consistent with this disclosure, Expert food elementcompatibility table 300 may include a single table and/or a plurality oftables; plurality of tables may include tables for particular categoriesof food elements such as a vegetable table, a fruit table, an animalprotein table, a seafood table, a spice table, a fat table, a graintable, and the like(not shown), to name a few non-limiting examplespresented for illustrative purposes only.

With continued reference to FIG. 3, one or more database tables inexpert knowledge database 136 may include, an expert body data table 316may list one or more body datums as described by experts, and one ormore compatible food products associated with body datum. For example, abody datum such as a blood test showing elevated glucose levels may beassociated with one or more compatible food products such as animalprotein, vegetables, and oils. As a further example an expertnutritional biomarker table 320 may list one or more nutritionalbiomarkers as described and input by experts and associatedphysiological classifications one or more nutritional biomarkers may beclassified into as well as desired dietary state. For instance andwithout limitation, an expert biomarker table 320 may include one ormore tables detailing biomarkers commonly associated with a particulardiet such as Paleo diet or ketogenic diet and the like. In yet anothernon-limiting example, expert biomarker table 320 may include one or moretables detailing biomarkers commonly associated with a particularphysiological system such as the gastrointestinal system or theneurological system. As an additional example, an expert biomarkerextraction table 324 may include information pertaining to biologicalextraction and/or medical test or collection necessary to obtain aparticular nutritional biomarker, such as for example a tissue samplethat may include a urine sample, blood sample, hair sample,cerebrospinal fluid sample, buccal sample, sputum sample, and the like.As an additional example, an expert vibrant compatibility table 328 mayinclude one or more tables detailing vibrant compatibility plan 192associated with one or more elements of body data, biomarker data andthe like. For example, expert vibrant compatibility table 328 mayinclude information detailing hierarchies of compatible food elementsfor a particular body datum such as a body datum reflecting lactoseintolerance may contain compatible food elements ranked as highlycompatible to include buckwheat, amaranth, and celery ranked as highlycompatible while cow yogurt, goat cheese, and cow milk may be ranked asbeing of very low compatibility. Tables presented above are presentedfor exemplary purposes only; persons skilled in the art will be aware ofvarious ways in which data may be organized in expert knowledge database136 consistently with this disclosure.

Referring now to FIG. 4, an exemplary embodiment of unsupervisedlearning module 156 is illustrated. Unsupervised learning may includeany of the unsupervised learning processes as described herein,Unsupervised learning module 156 generates at least a hierarchicalclustering model 160 to output at least a compatible food element 400 asa function of the at least a composition datum 108 and the at least acorrelated dataset. Correlated dataset may be selected from bodydatabase 124 as described below in more detail in reference to FIG. 5,Body database 124 may contain data describing different users withdifferent nutritional biomarkers and demographics of users, which may beorganized into categories contained within body database 124 asdescribed above in more detail in reference to FIG. 2. Hierarchicalclustering model 160 may group and/or segment datasets into hierarchyclusters including both agglomerative and divisive clusters.Agglomerative clusters may include a bottom up approach where eachobservation starts in its own cluster and pairs of clusters are mergedas one moves up the hierarchy. Divisive clusters may include a top downapproach where all observations may start in one cluster and splits areperformed recursively as one moves down the hierarchy. In an embodiment,hierarchical clustering model 160 may analyze datasets obtained frombody database 124 to find observations which may each initially form owncluster. Hierarchical clustering model 160 may then identify clustersthat are closest together and merge the two most similar clusters andcontinue until all clusters are merged together. Hierarchical clusteringmodel 160 may output a dendrogram which may describe the hierarchicalrelationship between the clusters. Distance between clusters that arecreated may be measured using a suitable metric. Distance may bemeasured between for example the two most similar parts of a clusterknown as single linkage, the two least similar bits of a cluster knownas complete-linkage, the center of the clusters known as average-linkageor by some other criterion which may be obtained based on input receivedfrom expert knowledge database 136 for example.

With continued reference to FIG. 4, unsupervised learning module 156 mayperform other unsupervised learning models to output at least acompatible food element output. Unsupervised learning module 156 maygenerate a data clustering model 404. Data clustering model 404 maygroup and/or segment datasets with shared attributes to extrapolatealgorithmic relationships. Data clustering model 404 may group data thathas been labelled, classified, and/or categorized. Data clustering modelmay identify commonalities in data and react based on the presence orabsence of such commonalities. For instance and without limitation, dataclustering model 404 may identify other data datasets that contain thesame or similar nutritional biomarker contained within composition datum108 or identify other datasets that contain users with similardemographics and/or background information. In an embodiment, dataclustering model 404 may cluster data and generate labels that may beutilized as training set data. Data clustering model 404 may utilizeother forms of data clustering algorithms including for example,hierarchical clustering, k-means, mixture models, OPTICS algorithm, andDBSCAN.

With continued reference to FIG. 4, unsupervised learning module 156 maygenerate an anomaly detection model 408. Anomaly detection model 408 mayinclude identification of rare items, events or observations that differsignificant from the majority of the data. Anomaly detection model 408may function to observe and find outliers. For instance and withoutlimitation, anomaly detect may find and examine data outliers such as anutritional biomarker that is not compatible with any food elements orthat is compatible with very few food elements.

With continued reference to FIG. 4, unsupervised learning module 156 maygenerate other unsupervised learning models 412. This may include forexample, neural networks, autoencoders, deep belief nets, Hebbianlearning, adversarial networks, self-organizing maps,expectation-maximization algorithm, method of moments, blind signalseparation techniques, principal component analysis, independentcomponent analysis, non-negative matrix factorization, singular valuedecomposition (not pictured).

Referring now to FIG. 5, an exemplary embodiment of hierarchicalclustering model 160 is illustrated. Composition datum 108 containing anelement of user body data 116 and an element of dietary state data 120is utilized to select at least a correlated dataset from body database.In an embodiment, datasets contained within body database 124 may beorganized into categories that each may be selected as a function ofcomposition datum 108 as described above in more detail in reference toFIG. 2. For example, first correlated dataset 500 may be categorized asdemographics and may contain datasets relating to demographics that maybe utilized when generating hierarchical clustering model 160. In suchan instance, second correlated dataset 504 may be categorized as fluidsample dataset that may contain datasets containing information relatingto a particular nutritional biomarker extracted from a fluid sample thatrelates to fluid sample contained within user body data 116. In such aninstance, third correlated dataset 508 may be categorized as diagnosticdataset, that may include contain data entries containing the samediagnosis as a user. In such an instance, fourth correlated dataset 512may be categorized as socioeconomic dataset, that may contain dataentries obtained from sources that are of a particular socioeconomicbackground related to user.

With continued reference to FIG. 5, datasets contained within bodydatabase may be obtained from a plurality of sources. Datasets containedwithin body database may be received from expert input such andcontained within expert knowledge database 136 as described above inmore detail in reference to FIG. 3. In an embodiment, experts such asleading functional medicine practitioners and scientists may provideinput through GUI 140 whereby such information may be contained withinexpert knowledge database 136. Datasets contained within body database124 may also be obtained from health record database 516, which maycontain datasets obtained from different sources such as deidentifiedmedical records as described below in more detail in reference to FIG.6. Datasets selected from body database 124 by at least a server 104 maybe utilized in combination with composition datum 108 to generatehierarchical clustering model 160. Hierarchical clustering model 160outputs at least a compatible food element. Compatible food elementincludes any of the compatible food elements as described herein.

Referring now to FIG. 6, an exemplary embodiment of health recorddatabase 516 is illustrated. Health record database 516 may include anydata structure for ordered storage and retrieval of data, which may beimplemented as a hardware or software module, and which may beimplemented as any database structure suitable for use as body database124. Health record database 516 may include datasets input from aplurality of sources that may be used as datasets by body database 124and/or training set database 180. One or more tables contained withinhealth record database 516 may include, as a non-limiting example,medical records table 600; medical records table 600 may include dataobtained from medical records. Medical records table 600 may includemedical record data that may be sourced from physicians, electronichealth records, hospitals, nursing homes, skilled nursing facilities,health insurance companies and the like. One or more tables containedwithin health record database 516 may include, as a non-limitingexample, patient survey table 604; patient survey table 604 may includedata obtained from patient surveys. Patient survey table 604 may includepatient surveys obtained from patients in specific geographic areas,specific socioeconomic backgrounds, patients with particular healthproblems, and the like. One or more tables contained within healthrecord database 516 may include, as a non-limiting example, open sourcetable 608; open source table 608 may include data obtained from opensources. Open source table 608 may include data obtained from opensources such as government sources and websites that may contain dataavailable for use by anyone. One or more tables contained within healthrecord database 516 may include, as a non-limiting example, clinicaltrial table 612; clinical trial table 612 may include data obtained fromclinical trials. Clinical trial table 612 may include informationobtained for example from clinical trials at research centers andacademic institutions.

Referring now to FIG. 7, an exemplary embodiment of food elementcompatibility index value database 168 is illustrated. Food elementcompatibility index value database 168 may include any data structurefor ordered storage and retrieval of data, which may be implemented as ahardware or software module, and which may be implemented as anydatabase structure suitable for use as body database 124. Food elementcompatibility index value database 168 may contain informationdescribing compatible substance index values for individual foodelements. Food element compatibility index value database 168 may beconsulted by at least a server 104 when generating at least a vibrantcompatibility plan 192. Food element compatibility index value database168 may be consulted by at least a server 104 when ranking foodelements. Food element compatibility index value database 168 maycontain food element compatibility index value for individual foodelements and each compatibility index value for each food linked to aparticular body datum, nutritional biomarker, and/or dietary state. Foodelement compatibility index value database 168 may also contain foodelement compatibility index values for combinations of multiple foodelements and compatibility of a second compatibility food element as afunction of selecting a first compatibility food element based on foodelement compatibility index value as described above in more detail inreference to FIG. 1. One or more database tables contained within foodelement compatibility index value database 168 may include Jerusalemartichoke table 700; Jerusalem artichoke table 700 may includecompatibility index values for Jerusalem artichoke for any givennutritional biomarker, body datum, and compatibility index values forJerusalem artichoke as compared to other compatible food elements forany given biomarker and/or body datum. For example, Jerusalem artichokemay have a high compatibility index value for a biomarker that shows lowgastrointestinal levels of Streptococcus and Lactobacillus but may havea low compatibility index value for a biomarker that shows overgrowth ofCandida. One or more database tables contained within food elementcompatibility index value database 168 may include sauerkraut table 704;sauerkraut table 704 may include compatibility index values forsauerkraut for any given nutritional biomarker, body datum, andcompatibility index values for sauerkraut as compared to othercompatible food elements for any given biomarker and/or body datum. Forexample and without limitation, sauerkraut may have a high compatibilityindex value for a dietary state such as paled but may have a lowcompatibility index value for a dietary state such as yeast-free diet.One or more database tables contained within food element compatibilityindex value database 168 may include tarragon table 708; tarragon table708 may include compatibility index values for tarragon for any givennutritional biomarker, body datum, and compatibility index values fortarragon as compared to other compatible food elements for any givenbiomarker and/or body datum. One or more database tables containedwithin food element compatibility index value database 168 may includeavocado oil table 712; avocado oil 712 may include may includecompatibility index values for avocado oil for any given nutritionalbiomarker, body datum, and compatibility index values for avocado ascompared to other compatible food elements for any given biomarkerand/or body datum. For example, avocado oil may contain a highcompatibility index value when substituted to selected as a function ofan avocado. One or more database tables contained within food elementcompatibility index value database 168 may include rainbow trout table716; rainbow trout table 716 may include may include compatibility indexvalues for rainbow trout for any given nutritional biomarker, bodydatum, and compatibility index values for avocado as compared to othercompatible food elements for any given biomarker and/or body datum. Forexample, rainbow trout may have a high compatibility index value ascompared to compatibility index value for brown trout, indicating a highlikelihood that rainbow trout may be substituted and/or selected as afunction first selecting brown trout. One or more database tablescontained within food element compatibility index value database 168 mayinclude tempeh table 720; tempeh table 720 may include compatibilityindex values for tempeh for any given nutritional biomarker, body datum,and compatibility index values for avocado as compared to othercompatible food elements for any given biomarker and/or body datum. Forexample, tempeh may contain a high compatibility index value for adietary state such as vegan diet but a low compatibility index value fora dietary state such as ketogenic. Tables contained within food elementcompatibility index value database may include other foods including forexample, chestnuts; coffee, cantaloupe melon, pistachios, arugula,bamboo shoots, beet greens, broccoli, burdock root, Italian artichoke,asparagus, beet, bok choy, Brussel sprouts, cabbage, celery (notpicture). Persons skilled in the art upon reviewing the entirety of thisdisclosure, will be aware of various forms which may be suitable for useas compatibility index value database consistently with this disclosure.

Referring now to FIG. 8, an exemplary embodiment of Jerusalem artichoketable 700 is illustrated. For instance and without limitation, Jerusalemartichoke table 700 may contain individual compatibility index valuescores for Jerusalem artichoke as compared to other biomarkers, bodydatums, dietary states, and food elements. For instance and withoutlimitation, Jerusalem artichoke table 700 may contain a first column 800containing other biomarkers, body datums, dietary states, and foodelements. For instance and without limitation, Jerusalem artichoke table700 may contain a second column 804 containing the specificcompatibility index value for a Jerusalem artichoke for the particularitem of comparison located in column 1 800. For instance and withoutlimitation, Jerusalem artichoke table 700 may contain a firstnutritional biomarker 808 and a compatibility index value 812 for aJerusalem artichoke linked to first nutritional biomarker 808. Forinstance and without limitation, Jerusalem artichoke table 700 maycontain a first body datum 816 and a compatibility index value 820 for aJerusalem artichoke linked to first body datum 816. For instance andwithout limitation, Jerusalem artichoke table 700 may contain a firstdietary state 824 and a compatibility index value 828 for a Jerusalemartichoke linked to first dietary state 824. For instance and withoutlimitation, Jerusalem artichoke table 700 may contain a first foodelement 832 and a compatibility index value 836 for a Jerusalemartichoke linked to first food element 832. In an embodiment, Jerusalemartichoke table 700 may contain n entries of nutritional biomarkers, nentries of body datums, n entries of dietary states, and n entries offood elements. In an embodiment, each food element may contain a tablelisting compatibilities of each food element to each item containedwithin column 1 to compatibility index values contained within column 2as described above in reference to FIG. 7.

Referring now to FIG. 9, an exemplary embodiment of food element profiledatabase 172 is illustrated. Food element profile database 172 mayinclude any data structure for ordered storage and retrieval of data,which may be implemented as a hardware or software module, and which maybe implemented as any database structure suitable for use as bodydatabase 124. Food element profile database 172 may contain informationcontaining nutrient density information about a particular food element.Data contained within food element profile database 172 may be utilizedto calculate food element compatibility index value and when generatingvibrant compatibility plan 192 t. One or more tables contained withinfood element profile database 172 may include for instance mineral scoretable 900; mineral score table 900 may include information describingminerals contained within a particular food element. For example,mineral score table 900 may include minerals such as iron and calciumcontained within a particular food element such as a banana or mineralscontained within multiple food elements such as for example orangecantaloupe melon, chicken, and black lentils. One or more tablescontained within food element profile database 172 may include forinstance, vitamin score table 904; vitamin score table 904 may includeinformation describing vitamins contained within a particular foodelement. For example, vitamin score table 904 may include vitamins suchas Vitamin C, Vitamin E, and Vitamin D contained within one or more foodelements such as blackstrap molasses, buckwheat, and oatmeal. One ormore tables contained within food element profile database 172 mayinclude for instance, electrolyte score table 908; electrolyte scoretable may include information describing electrolytes contained within aparticular food element. For example, electrolyte score table 908 mayinclude information describing electrolyte quantities such as potassium,and sodium contained within a food element such as lamb or walnuts. Oneor more tables contained within food element profile database 172 mayinclude for instance, antioxidant score table 912; antioxidant scoretable 912 may include information describing antioxidants containedwithin a particular food element and/or plurality of food elements. Forexample, antioxidant table 912 may include information describingantioxidants that may aid in slowing oxidation or electron transfer suchas for example cranberries, blueberries, grapes, raspberry, elderberry,black currants, pomegranates, plums and the like. One or more tablescontained within food element profile database 172 may include forinstance, amino acid score table 916; amino acid score table 916 mayinclude information describing amino acids contained within a particularfood element. For example, amino acid score table 916 may includeinformation describing amino acids contained within a particular foodelement including for example amino acids such as valine, alanine,arginine, glutamine, lysine, aspartic acid, glutamic, acid and the like.For example, amino acid score table 916 may include informationdescribing quantities of amino acids such as valine and leucinecontained within white turkey or quantities of lysine and aspartic acidcontained within tofu. One or more tables contained within food elementprofile database 172 may include for instance, micronutrient score table920; micronutrient score table 920 may include information describingquantities of micronutrients contained within a particular food element.For example, micronutrient score table 920 may include informationdescribing quantities of folate and iodine contained within a particularfood element. Information contained within food element profile database172 may include other score tables including for example, metabolites,fatty acids, fat content, saturated fat content, protein content,carbohydrate content, (not pictured) and the like. Persons skilled inthe art upon reviewing the entirety of this disclosure, will be aware ofvarious forms which may be suitable for use as food element profiledatabase 172 consistently with this disclosure.

Referring now to FIG. 10, an exemplary embodiment of supervised learningmodule 176 is illustrated. Supervised learning module 176 may include atleast a label learner 1000 designed and configured to create at least asupervised machine learning model 188 using the at least a firsttraining set 184 wherein the at least at supervised machine learningmodel 188 relates body data to compatible food elements. First trainingset 184 may be selected from training set database 180. Supervisedlearning module 176 may be configured to perform any supervisedmachine-learning algorithm as described above in reference to FIG. 1,This may include for example, support vector machines, linearregression, logistic regression, naïve Bayes, linear discriminantanalysis, decision trees, k-nearest neighbor algorithm, neural networks,and similarity learning. Supervised machine-learning module may generatea first supervised machine learning model 188 which may be utilized togenerate compatible food element output 400. In an embodiment, firsttraining set 184 may include the at least a correlated dataset. Firsttraining set 184 may be selected by categorizing the at least a userbody datum to contain at least a physiological label and select at leasta first training set 184 as a function of the at least a physiologicallabel. In an embodiment, training sets contained within training setdatabase 180 may be organized and categorized by groupings ofphysiological labels as described in more detail below in reference toFIG. 11.

With continued reference to FIG. 10, supervised learning module 176 mayinclude at least a label learner 1000 which may include any hardware orsoftware module. At least a label learner 1000 may select first trainingset 184 and/or second training set 1004 from training set database 180and/or body database 124. In an embodiment, at least a label learner1000 may generate different supervised models 186 to create learnedassociations between training sets and inputs and outputs utilized toselect training sets such as correlations between body data andcorrelated compatible food elements. In an embodiment, at least a labellearner 1000 may select datasets from body database 124 to be utilizedas training sets. Datasets contained within body database 124 and/ortraining set database 180 may be generated based on inputs by expertsand sources contained within expert knowledge database 136 and/or healthrecord database 516 through submissions generated by experts atgraphical user interface 140.

With continued reference to FIG. 10, supervised learning module 176 maygenerate compatible food element output 400 by executing a lazy learningmodule 1008 as a function of a user body datum and a compatible foodelement output 400. A lazy-learning process and/or protocol, which mayalternatively be referred to as a “lazy loading” or “call-when-needed”process and/or protocol, may be a process whereby machine learning isconducted upon receipt of an input to be converted to an output, bycombining the input and training set to derive the algorithm to be usedto produce the output on demand. For instance, an initial set ofsimulations may be performed to cover a “first guess” at an antidoteassociated with at least a user input datum, using at least a trainingset. As a non-limiting example, an initial heuristic may include aranking of compatible food elements according to relation to a test typeof at least a user body datum, one or more categories of body dataidentified in test type of at least a composition datum 108, and/or oneor more values detected in at least a user composition datum 108 sample;ranking may include, without limitation, ranking according tosignificance scores of associations between elements of body data andcompatible food elements, for instance as calculated as described above.Heuristic may include selecting some number of highest-rankingassociations and/or compatible food elements. Lazy learning module 1008may alternatively or additionally implement any suitable “lazy learning”algorithm, including without limitation a K-nearest neighbors algorithm,a lazy naïve Bayes algorithm; or the like; persons skilled in the art;upon reviewing the entirety of this disclosure, will be aware of variouslazy-learning algorithms that may be applied to generate antidotes asdescribed in this disclosure, including without limitation lazy learningapplications of machine-learning algorithms as described in furtherdetail below.

Referring now to FIG. 11, an exemplary embodiment of training setdatabase 180 is illustrated. Training set database 180 may include anydata structure for ordered storage and retrieval of data, which may beimplemented as a hardware or software module, and which may beimplemented as any database structure suitable for use as body database124. For instance and without limitation, one or more database tablescontained within training set database 180 may include body databaselink table 1100; body database link table 1100 may contain informationlinking datasets contained within body database. For example, datasetcontained within body database may also be contained within training setdatabase 180 which may be linked through body database link table 1100.In yet another non-limiting example, body database link table 1100 maycontain information linking datasets contained within body database todatasets contained within training set database 180, such as whendataset and training set may include data sourced from the same user orsame cohort of users. For instance and without limitation, one or moredatabase tables contained within training set database 180 may includetissue sample table 1104; tissue sample table 1104 may contain trainingsets containing tissue samples that may contain one or more nutritionalbiomarkers which may be correlated to one or more compatible foodelement outputs. Tissue sample table 1104 may include tissue samplessuch as blood, cerebrospinal fluid, urine, blood plasma, synovial fluid,amniotic fluid, lymph, tears, saliva, semen, aqueous humor, vaginallubrication, bile, mucus, vitreous body, gastric acid, which may becorrelated to a compatible food element output. For instance and withoutlimitation, one or more database tables contained within training setdatabase 180 may include demographic table 1108; demographic table 1108may contain training sets containing demographics that may contain bodydata and one or more demographics correlated to one or more compatiblefood element outputs. Demographics may include residence location,geographical area where a subject may live, age, race, ethnicity,gender, marital status, income, education, employment and the like. Forinstance and without limitation, one or more database tables containedwithin training set database 180 may include nutrition biomarker table1112; nutrition biomarker table 1112 may include one or more nutritionbiomarkers correlated to one or more compatible food elements. Forexample, a nutrition biomarker contained within a training set maycontain a biomarker such as Candida overgrowth in the gastrointestinaltract correlated to a compatible food such as spinach. For instance andwithout limitation, one or more database tables contained withintraining set database 180 may include compatible food element table1116; compatible food element table 116 may include compatible foodelements correlated to other compatible food elements. For example,compatible food element table 1116 may include a first compatible foodelement correlated to a second compatible food element. For instance andwithout limitation, one or more database tables contained withintraining set database 180 may include physiological table 1120;physiological table 1120 may include one or more physiological labelscorrelated to one or more compatible food elements. For example,physiological table 1120 may include a physiological label such asneurological correlated to one more compatible food elements containingB Vitamins such as buckwheat, oats, amaranth, and barley. Personsskilled in the art upon reviewing the entirety of this disclosure, willbe aware of various forms which may be suitable for use as training setdatabase 180 consistently with this disclosure.

Referring now to FIG. 12, an exemplary embodiment of filter database1200 is illustrated. Filter database 1200 may include any data structurefor ordered storage and retrieval of data, which may be implemented as ahardware or software module, and which may be implemented as anydatabase structure suitable for use as body database 124. Filterdatabase 1200 may include information input by user regarding user'sfavorite food elements and dietary restrictions. Filter database 1200may be consulted by at least a server 104 when generating at least avibrant compatible plan to filter off compatible food elements that maynot match a user's food and eating preferences. One or more tablescontained within filter database 1200 may include favorite food elementtable 1204; favorite food element table 1204 may include a user'spreference for favorite food elements. For example, favorite foodelement table 1204 may include the top ten food elements that a userconsumes on a weekly basis for instance. One or more tables containedwithin filter database 1200 may include dietary elimination table 1208;dietary elimination table 1208 may include information pertaining to aparticular food element or food group that a user may need to eliminatedue to a user's own personal preference to not consume a particular foodelement for ethical or moral reasons or due to an allergy such as ananaphylactic allergy to a food element such as tree nuts or dairyproducts. Persons skilled in the art upon reviewing the entirety of thisdisclosure, will be aware of various forms which may be suitable for useas filter database consistently with this disclosure.

Referring now to FIG. 13, an exemplary embodiment of a method 1300 ofgenerating a dietary state plan using artificial intelligence isillustrated. At step 1305 at least a server 104 receives at least acomposition datum 108 from a user client device 112 generated as afunction of at least a user conclusive label and at least a user dietaryresponse wherein the at least a composition datum 108 further comprisesat least an element of user body data 116 and at least an element ofdesired dietary state data. At least a composition datum 108 may bereceived by at least a server 104 utilizing any of the networkmethodology as described herein. At least a composition datum 108 mayinclude any of the composition datum 108 as described herein. At leastan element of user body data 116 may include any of the user body data116 as described herein. For example, at least an element of user bodydata 116 may include a nutritional biomarker such as for example agenotype marking of the ACE gene that regulates blood pressure. In anembodiment, at least an element of user body data 116 may include a userself-reported elimination of a particular food element such as forexample, a user's self-reported elimination of gluten products due tobloating or a user's self-reported elimination of dairy containingproducts due to lose stools. At least an element of desired dietarystate data 120 may include any of the dietary state data 120 asdescribed herein. For example, at least an element of desired dietarystate data 120 may include a user's desire or current diet that consistsof the paleo diet or a vegan diet. In an embodiment, at least an elementof body data and at least an element of desired dietary state data 120may include the same inputs. For example, at least a composition datum108 may include at least an element of user body data 116 that includesa user's self-reported elimination of all animal containing foodproducts and at least an element of desired dietary state data 120 thatincludes a current diet of a vegan diet which does not contain anyanimal products. In yet another non-limiting example, at least acomposition datum 108 may include a user's self-reported elimination ofall grain containing foods and at least an element of desired dietarystate data 120 that includes a description of a desire to consume apaleo diet. At least a composition datum may be generated as a functionof at least a user conclusive label and at least a user dietaryresponse. This may be done utilizing any of the methodologies asdescribed above in reference to FIG. 1. For example, at least a userconclusive label such as a diagnosis of heart disease may be utilized togenerate at composition datum that includes body data such as anelevated lipid panel and desired dietary state data that includes alow-fat diet. In yet another non-limiting example, at least a userdietary response such as a preference for gluten free foods due tostomach bloating and cramping may be utilized by a user to generate atleast a composition datum that includes body data such as a complaint ofstomach bloating and cramping and desired dietary state data thatincludes a gluten free diet. In an embodiment, a user may not have aconclusive label such as if a user has not been diagnosed with anymedical condition or if a user previously healed a medical condition ordisease state. In an embodiment, a user may generate a dietary responsethat may not contain a preference for a particular diet or way ofeating.

With continued reference to FIG. 13, at least an element of desireddietary state data 120 may be received as a function of a userconclusive label. User conclusive label may include any of the userconclusive labels as described above in reference to FIG. 11. Forexample, user conclusive label may contain diagnostic information suchas a certain physical condition that a user may be dealing with. In suchan instance, an element of desired dietary state data 120 may begenerated by a functional medicine health professional through graphicaluser interface 140 for instance. For example and without limitation,user with a conclusive label containing a diagnosis of diabetes may beutilized by a functional medicine physician to generate at least anelement of desired dietary state data 120 that includes a recommendationof a low carbohydrate diet so as to minimize insulin spikes that may beexperienced by a use with diabetes. In yet another non-limiting example,a conclusive label containing a diagnosis of two copies of the APOE4gene associated with a significant increased risk for Alzheimer'sdisease may be utilized by a health professional such as a dietician ornutritionist to generate at least a desired dietary state that includesa vegan diet. Receiving at least an element of desired dietary statedata 120 may be received as a function of a user generated response. Forinstance and without limitation, a user may enter responses to questionsabout dietary preferences and eating habits through for examplegraphical user interface 140, which may then generate a desired dietarystate for a user. For example, a user may enter information throughgraphical user interface 140 such as habitually enjoying three meals perday which may be utilized to generate a desired dietary state such asthe Mediterranean diet. In yet another non-limiting example, a user mayenter a response to a question that describes user's aversion to certainfood elements such as animal protein, which may be used to generate adesired dietary state that recommends a vegetarian diet or a vegan diet.

With continued reference to FIG. 13, at least a server 104 is configuredto receive at least a user conclusive label containing at least anincompatible food element as a function of at least a conclusive labelneutralizer. Conclusive label neutralizer may include any of theconclusive label neutralizers as described above in reference to FIG. 1.For example, a conclusive label neutralizer such as a medication used totreat epilepsy may be utilized to generate at least an incompatible foodelement that includes grapefruit and grapefruit containing foodelements. In yet another non-limiting example, a conclusive labelneutralizer containing an antibiotic such as metronidazole may beutilized to generate at least an incompatible food element that includesalcohol and alcohol containing food elements. At least a server 104 isconfigured to receive at least a user dietary response containing atleast an acute vibrancy input, at least a chronic vibrancy input, and atleast a longevity vibrancy input. Acute vibrancy input may include anyof the acute vibrancy inputs as described above in reference to FIG. 1.For example, acute vibrancy input may include a user's short termdietary response which may include a desire to eliminate simplecarbohydrates for three weeks in order to lose weight and fit into adress for a special event. Chronic vibrancy input may include any of thechronic vibrancy inputs as described above in reference to FIG. 1.Chronic vibrancy input may include a user's chronic dietary responsewhich may include a user's desire to eliminate white sugar from diet ora desire to not consume trans-fats. Longevity vibrancy input may includeany of the longevity vibrancy inputs as described above in reference toFIG. 1. Longevity vibrancy input may include a user's lifelong dietaryresponse such as a desire to eliminate all artificial sweeteners fromthe diet or a desire to consume at least three servings of vegetableseach day.

With continued reference to FIG. 13, at step 1310 at least a server 104selects at least a correlated dataset containing a plurality of dataentries wherein each dataset contains at least a datum of body data andat least a correlated compatible food element as a function of the atleast a composition datum 108. Datasets may include any of the datasetsas described herein. Datasets may be selected from body database 124.Datasets contained within body database 124 may be categorized and/ororganized by any of the methodologies as described above in reference toFIG. 1 and FIG. 2. In an embodiment, at least a dataset may be selectedby extracting at least a physiological trait from at least a compositiondatum 108 and matching the at least a physiological trait to at least acorrelated dataset containing at least an element of the at least aphysiological trait. At least a physiological trait may be extractedfrom at least a composition datum 108 utilizing parsing module 148and/or language processing module 144 as described above in more detailin reference to FIG. 1. Physiological trait may include any of thephysiological traits as described herein. For example, parsing module148 and language processing module 144 may extract at least aphysiological trait such as a particular nutrition biomarker which maybe utilized to match the particular nutrition biomarker to a datasetcontained within body database that contains the particular nutritionbiomarker. In an embodiment, datasets contained within body database 124may be organized and categorized according to physiological traits. Forexample, a physiological trait relating to MTHFR genotype extracted fromat least a composition datum 108 may be matched to a dataset containedwithin body database that is categorized as belonging to a category ofphysiological traits such as gene nutrition biomarkers. In yet anothernon-limiting example, a physiological trait relating to a microbiomepopulation of a user's gut may be matched to a dataset contained withinbody database that is categorized as belonging to a category ofphysiological traits such as microbiome nutrition biomarkers.

With continued reference to FIG. 13, at step 1315 at least a servercreates at least an unsupervised machine-learning algorithm the at leastan unsupervised machine-learning algorithm further comprises generatinga hierarchical clustering model 160 to output at least a compatible foodelement as a function of the at least a composition datum 108 and the atleast a correlated dataset. Unsupervised machine-learning algorithm mayinclude any of the unsupervised machine-learning algorithms as describedherein. Hierarchical clustering model 160 may include any of thehierarchical clustering model 160 as described above in reference toFIGS. 1-13. Hierarchical clustering model 160 may output at least acompatible food element output. Compatible food element output mayinclude any of the compatible food element outputs as described above inreference to FIGS. 1-13. In an embodiment unsupervised machine-learningalgorithm may be created by an unsupervised learning module 176.Unsupervised learning module 156 may generate other unsupervisedlearning models including for example anomaly detection model, dataclustering model, and other unsupervised learning models. In anembodiment, datasets utilized to generate unsupervised learning modelsincluding hierarchical clustering model 160 may be obtained from bodydatabase as described above in more detail in reference to FIG. 4. In anembodiment, a plurality of datasets may be selected from body databaseand utilized to generate hierarchical clustering model 160 as describedabove in more detail in reference to FIG. 5. Datasets contained withinbody database may be obtained from expert input and from health recorddatabase. For example, datasets contained within body database may beobtained from medical records that have personal identifying informationremoved or from the results of patient surveys as described above inmore detail in reference to FIG. 6.

With continued reference to FIG. 13, system 100 may create a supervisedmachine learning model 188 in addition to unsupervised machine learningmodel. Supervised machine learning model 188 may include any of thesupervised machine learning model 188 as described above in reference toFIGS. 1-13. In an embodiment, at least a server 104 and/or supervisedlearning module 176 may select at least a first training set 184, createat least a supervised machine learning model 188 using the at least afirst training set 184 wherein the at least a supervised machinelearning model 188 relates body data to compatible food elements andgenerates at least a compatible food element output as a function of theat least a composition datum 108 and the at least a first training set184. Training set may include any of the training sets and training dataas described above in reference to FIGS. 1-13. In an embodiment, firsttraining set 184 may including selecting the at least a correlateddataset to be utilized as first training set 184. First training set 184may be selected by categorizing at least a composition datum 108 tocontain at least a physiological label and selecting at least a firsttraining set 184 as a function of the at least a physiological label.Physiological label may include any of the physiological labels addescribed above in reference to FIGS. 1-13, For example, a physiologicallabel may indicate a particular body dimension that is categorized ascontaining a particular root cause pillars of disease. In an embodiment,at least a composition datum 108 categorized as relating to gut wallbody dimension may be utilized to select at least a first training set184 that relates gut wall data to compatible food elements. In such aninstance, training sets may be organized within training set database180 according to body dimension for example.

With continued reference to FIG. 13, at step 1320 at least a servergenerates at least a vibrant compatibility plan 192 wherein the at leasta vibrant compatibility plan 192 further comprises a plurality ofcompatible food elements each containing at least a food elementcompatibility index value as a function of the at least a hierarchicalclustering model 160. Food element compatibility index value may includeany of the food element compatibility index values as described above inreference to FIGS. 1-13. In an embodiment, generating at least a vibrantcompatibility plan 192 may include retrieving at least a food elementcompatibility index value correlated to at least a food element from adatabase and ranking the at least a food element as a function of the atleast a food element compatibility index value. Food elementcompatibility index value may be contained within a food elementcompatibility index value database 168 as described above in referenceto FIG. 7 and FIG. 8. Food element compatibility index value may becalculated as a function of the at least a composition datum 108 and theat least a first food element profile. First food element profile mayinclude any of the first food element profiles as described above inreference to FIGS. 1-13. Food element profiles may be contained withinfood element profile database 172 as described above in reference toFIG. 9, In an embodiment, food element profile database 172 may containscores for different components of a food element including for example,a mineral score, a vitamin score, an electrolyte score, an antioxidantscore, an amino acid score, and a micronutrient score.

With continued reference to FIG. 13, generating at least a vibrantcompatibility plan 192 may include generating a sequencing instructionset wherein the sequencing instruction set contains at least an optimalcombination of at least a first compatible food element and at least asecond compatible food element as a function of the at least a desireddietary state. In an embodiment, sequencing instruction set may includean optimal combination of food elements as a function of at least adesired dietary state. For example, sequencing instruction set mayinclude an optimal combination of grass fed beef and broccoli for a userwith a desired dietary state of a paleo diet while sequencinginstruction set may include an optimal combination of millet andeggplant for a user with a desired dietary state of macrobiotic diet. Inan embodiment, vibrant compatibility plan may include a ranking ofcompatible food elements as a function of food element compatibilityindex value score. For instance and without limitation, vibrantcompatibility plan may include a hierarchy of compatible food elementslisted in descending order of compatibility whereby food elementscontaining the highest food element compatibility index score may belisted first.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 14 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 1400 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 1400 includes a processor 1404 and a memory1408 that communicate with each other, and with other components, via abus 1412. Bus 1412 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Memory 1408 may include various components (e.g., machine-readablemedia) including, but not limited to, a random access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 1416 (BIOS), including basic routines thathelp to transfer information between elements within computer system1400, such as during start-up, may be stored in memory 1408. Memory 1408may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 1420 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 1408 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 1400 may also include a storage device 1424. Examples ofa storage device (e.g., storage device 1424) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 1424 may beconnected to bus 1412 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device1424 (or one or more components thereof) may be removably interfacedwith computer system 1400 (e.g., via an external port connector (notshown)). Particularly, storage device 1424 and an associatedmachine-readable medium 1428 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 1400. In one example,software 1420 may reside, completely or partially, withinmachine-readable medium 1428. In another example, software 1420 mayreside, completely or partially, within processor 1404.

Computer system 1400 may also include an input device 1432. In oneexample, a user of computer system 1400 may enter commands and/or otherinformation into computer system 1400 via input device 1432. Examples ofan input device 1432 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 1432may be interfaced to bus 1412 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 1412, and any combinations thereof. Input device 1432may include a touch screen interface that may be a part of or separatefrom display 1436, discussed further below. Input device 1432 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 1400 via storage device 1424 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 1440. A networkinterface device, such as network interface device 1440, may be utilizedfor connecting computer system 1400 to one or more of a variety ofnetworks, such as network 1444, and one or more remote devices 1448connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 1444, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 1420, etc.) may be communicated to and/or fromcomputer system 1400 via network interface device 1440.

Computer system 1400 may further include a video display adapter 1452for communicating a displayable image to a display device, such asdisplay device 1436. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 1452 and display device 1436 maybe utilized in combination with processor 1404 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 1400 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 1412 via a peripheral interface 1456.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for generating a vibrant compatibilityplan using artificial intelligence, the system comprising: at least aserver, the at least a server designed and configured to: receive atleast a composition datum from a user client device, generated as afunction of at least a user conclusive label and at least a user dietaryresponse, wherein the at least a composition datum further comprises atleast an element of user body data and at least an element of desireddietary state data and the at least a user conclusive label contains atleast an incompatible food element generated as a function of at least aconclusive label neutralizer; select at least a correlated datasetcontaining a plurality of data entries wherein the at least a correlateddataset contains at least a datum of body data and at least a correlatedcompatible food element as a function of the at least a compositiondatum; create at least an unsupervised machine-learning model whereinthe at least an unsupervised machine-learning model further comprisesgenerating a hierarchical clustering model to output at least acompatible food element as a function of the at least a compositiondatum and the at least a correlated dataset; and generate at least avibrant compatibility plan wherein the at least a vibrant compatibilityplan further comprises a plurality of compatible food elements eachcontaining at least a food element compatibility index value score as afunction of the at least a hierarchical clustering model.
 2. The systemof claim 1, wherein the at least a server is further configured toreceive at least a user dietary response containing at least an acutevibrancy input, at least a chronic vibrancy input, and at least alongevity vibrancy input.
 3. The system of claim 1, wherein the at leasta server is further configured to: extract at least a physiologicaltrait from the at least a composition datum; and match the at least aphysiological trait to at least a correlated dataset containing at leastan element of the at least a physiological trait.
 4. The system of claim1, wherein the at least a server is further configured to: retrieve atleast a food element compatibility index value correlated to at least afood element from a database; and rank the at least a food element as afunction of the at least a food element compatibility index value. 5.The system of claim 4, wherein the at least a food element compatibilityindex value is calculated as a function of the at least a compositiondatum and at least a food element profile.
 6. The system of claim 1,wherein the at least a server further comprises a supervised moduleoperating on the at least a server wherein the supervised module isdesigned and configured to: receive at least a composition datum; selectat least a first training set; create at least a supervisedmachine-learning model using the at least a first training set whereinthe at least a supervised machine-learning model relates body data tocompatible food elements; and generate at least a compatible foodelement output as function of the at least a composition datum and theat least a first training set.
 7. The system of claim 6, wherein the atleast a first training set further comprises the at least a correlateddataset.
 8. The system of claim 6, wherein the at least a supervisedmodule is further configured to: categorize the at least an element ofuser body data to contain at least a physiological label; and select atleast a first training set as a function of the at least a physiologicallabel.
 9. The system of claim 1, wherein the at least a server isfurther configured to generate at least a vibrant compatibility plancontaining a sequencing instruction set, wherein the sequencinginstruction set contains at least an optimal combination of at least afirst compatible food element and at least a second compatible foodelement as a function of the at least an element of desired dietarystate data.
 10. A method of generating a vibrant compatibility planusing artificial intelligence, the method comprising: receiving by atleast a server at least a composition datum from a user client device,generated as a function of at least a user conclusive label and at leasta user dietary response, wherein the at least a composition datumfurther comprises at least an element of user body data and at least anelement of desired dietary state data and the at least a user conclusivelabel contains at least an incompatible food element generated as afunction of at least a conclusive label neutralizer; selecting by the atleast a server at least a correlated dataset containing a plurality ofdata entries wherein the at least a correlated dataset contains at leasta datum of body data and at least a correlated compatible food elementas a function of the at least a composition datum; creating by the atleast a server at least an unsupervised machine-learning model whereinthe at least an unsupervised machine-learning model further comprisesgenerating a hierarchical clustering model to output at least acompatible food element as a function of the at least a compositiondatum and the at least a correlated dataset; and generating by the atleast a server at least a vibrant compatibility plan wherein the atleast a vibrant compatibility plan further comprises a plurality ofcompatible food elements each containing a least a food elementcompatibility index value score as a function of the at least ahierarchical clustering model.
 11. The method of claim 10, whereinreceiving at least a composition datum further comprises receiving atleast a user dietary response containing at least an acute vibrancyinput, at least a chronic vibrancy input, and at least a longevityvibrancy input.
 12. The method of claim 10, wherein selecting at least acorrelated dataset further comprises: extracting at least aphysiological trait from at least a composition datum; and matching theat least a physiological trait to at least a correlated datasetcontaining at least an element of the at least a physiological trait.13. The method of claim 10, wherein generating at least a vibrantcompatibility plan further comprises: retrieving at least a food elementcompatibility index value correlated to at least a food element from adatabase; and ranking the at least a food element as a function of theat least a food element compatibility index value.
 14. The method ofclaim 13, wherein the at least a food element compatibility index valueis calculated as a function of the at least a composition datum and atleast a first food element profile.
 15. The method of claim 10 furthercomprising: receiving at least a composition datum; selecting at least afirst training set; creating at least a supervised machine-learningmodel using the at least a first training set wherein the at least asupervised machine-learning model relates body data to compatible foodelements; and generating at least a compatible food element a functionof the at least a composition datum and the at least a first trainingset.
 16. The method of claim 15, wherein selecting the at least a firsttraining set further comprises selecting the at least a correlateddataset.
 17. The method of claim 15, wherein selecting at least a firsttraining set further comprises: categorizing the at least a compositiondatum to contain at least a physiological label; and selecting at leasta first training set as a function of the at least a physiologicallabel.
 18. The method of claim 10 further comprising generating at leasta vibrant compatibility plan containing a sequencing instruction set,wherein the sequencing instruction set contains at least an optimalcombination of at least a first compatible food element and at least asecond compatible food element as a function of the at least an elementof desired dietary state data.